├── .gitignore ├── 01_Introductory_Concepts ├── 01_Python │ └── 01. Intro to Python.ipynb ├── 02_Pandas │ └── 01. Intro to Pandas.ipynb ├── 03_Matplotlib │ └── 01. Intro to Matplotlib.ipynb ├── 04_Numpy │ ├── 01. Intro to Numpy.ipynb │ ├── 01. Numpy_Template.ipynb │ └── NumPy_Assignments │ │ ├── NumPy_Assignment_Solution.ipynb │ │ └── NumPy_Assignment_Template.ipynb ├── 05_Linear_Algebra │ └── 01_Introduction_to_Linear_Algebra.ipynb └── imgs │ └── ML.png ├── 02_Machine_Learning_Tutorials ├── Day 02 — Simple Linear Regression.ipynb ├── Day 03 — Optimization with Gradient Descent (GD).ipynb └── Day 04 — Multiple Linear Regression (MLR).ipynb ├── Before codes ├── 03_Supervised_Learning │ ├── 01_underfit_good_fit_overfit.ipynb │ ├── 07. Univariate_linear_regression_scratch.ipynb │ ├── 09. Bayesian_Model.ipynb │ ├── 09. Logistic_Regression.ipynb │ ├── 10. LDA_QDA.ipynb │ ├── 11. KNN.ipynb │ ├── 12. KDTree_BallTree.ipynb │ ├── 13. SVM.ipynb │ ├── 15. Decision_Tree.ipynb │ ├── 18. Random Forest.ipynb │ └── Templates │ │ ├── Template_06. Univariate_linear_regression.ipynb │ │ ├── Template_07. Univariate_linear_regression_scratch.ipynb │ │ └── Template_08. multiple_linear_regression.ipynb ├── 04_Unsupervised_Learning │ ├── 14. Clustering.ipynb │ └── 16. PCA.ipynb └── 05_Neural_Networks │ └── 01_Advanced_MLP.ipynb ├── Data ├── Data.csv ├── Real_Estate_DataSet.csv ├── Regression │ ├── 50_Startups.csv │ ├── Advertising.csv │ ├── Automobile_data.csv │ └── World_Happiness_Report.csv ├── classification │ ├── Social_Network_Ads.csv │ └── breast-cancer-wisconsin.csv ├── multiple_arrays.npz ├── numpy_data.csv ├── numpy_data.txt ├── numpy_data_with_missing.csv ├── numpy_output.npy ├── numpy_output.txt └── pandas │ ├── Sales_data.csv │ ├── Sales_data.xlsx │ ├── adult.csv │ ├── sales_data.db │ └── sales_data.json ├── LICENSE ├── README.md ├── README_2.md └── pics ├── 50_startups.png ├── Broadcast_with_scalar.png ├── Gradient_computation.png ├── ML.png ├── MSE.png ├── Normalization.png ├── Standard_Scaling.png ├── adv.png ├── broadcast_array_with_2d.png ├── broadcast_mismatch.png ├── broadcastable_arrays.png ├── car_price_prediction.png ├── gradients_w_b.png ├── hypothesis_function_lr.png ├── not_broadcastable.png └── params_args.png /.gitignore: -------------------------------------------------------------------------------- 1 | .venv 2 | -------------------------------------------------------------------------------- /01_Introductory_Concepts/04_Numpy/NumPy_Assignments/NumPy_Assignment_Solution.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# 📝 **NumPy Assignment Series**" 8 | ] 9 | }, 10 | { 11 | "cell_type": "markdown", 12 | "metadata": {}, 13 | "source": [ 14 | "## Assignment 1: Array Creation and Initialization\n", 15 | "\n", 16 | "### **Concept:**\n", 17 | "Learn how to create and initialize NumPy arrays using different methods.\n", 18 | "\n", 19 | "### **Tasks:**\n", 20 | "\n", 21 | "1. **Create an array from a list:** \n", 22 | " Convert the following list into a NumPy array: `[1, 2, 3, 4, 5]`.\n", 23 | "\n", 24 | "2. **Create arrays using functions:** \n", 25 | " - Create an array of zeros with shape `(3, 3)`.\n", 26 | " - Create an array of ones with shape `(2, 4)`.\n", 27 | " - Create an array of evenly spaced values between 0 and 10 with a step of 2.\n", 28 | "\n", 29 | "3. **Create an identity matrix:** \n", 30 | " Create a 4x4 identity matrix.\n", 31 | "\n", 32 | "### **Solution:**" 33 | ] 34 | }, 35 | { 36 | "cell_type": "code", 37 | "execution_count": 1, 38 | "metadata": {}, 39 | "outputs": [ 40 | { 41 | "name": "stdout", 42 | "output_type": "stream", 43 | "text": [ 44 | "[1 2 3 4 5]\n", 45 | "[[0. 0. 0.]\n", 46 | " [0. 0. 0.]\n", 47 | " [0. 0. 0.]]\n", 48 | "[[1. 1. 1. 1.]\n", 49 | " [1. 1. 1. 1.]]\n", 50 | "[ 0 2 4 6 8 10]\n", 51 | "[[1. 0. 0. 0.]\n", 52 | " [0. 1. 0. 0.]\n", 53 | " [0. 0. 1. 0.]\n", 54 | " [0. 0. 0. 1.]]\n" 55 | ] 56 | } 57 | ], 58 | "source": [ 59 | "import numpy as np\n", 60 | "\n", 61 | "# 1. Create an array from a list\n", 62 | "arr1 = np.array([1, 2, 3, 4, 5])\n", 63 | "\n", 64 | "# 2. Create arrays using functions\n", 65 | "arr_zeros = np.zeros((3, 3))\n", 66 | "arr_ones = np.ones((2, 4))\n", 67 | "arr_linspace = np.arange(0, 11, 2)\n", 68 | "\n", 69 | "# 3. Create an identity matrix\n", 70 | "identity_matrix = np.eye(4)\n", 71 | "\n", 72 | "print(arr1)\n", 73 | "print(arr_zeros)\n", 74 | "print(arr_ones)\n", 75 | "print(arr_linspace)\n", 76 | "print(identity_matrix)" 77 | ] 78 | }, 79 | { 80 | "cell_type": "markdown", 81 | "metadata": {}, 82 | "source": [ 83 | "## Assignment 2: Array Manipulation\n", 84 | "\n", 85 | "### **Concept:**\n", 86 | "Learn how to reshape, flatten, and concatenate arrays.\n", 87 | "\n", 88 | "### **Tasks:**\n", 89 | "\n", 90 | "1. **Reshape an array:** \n", 91 | " Reshape the array `[1, 2, 3, 4, 5, 6]` into a 2x3 matrix.\n", 92 | "\n", 93 | "2. **Flatten a 2D array:** \n", 94 | " Flatten the following 2D array: \n", 95 | " ```\n", 96 | " [[1, 2, 3],\n", 97 | " [4, 5, 6]]\n", 98 | " ```\n", 99 | "\n", 100 | "3. **Concatenate arrays:** \n", 101 | " Concatenate the arrays `[1, 2, 3]` and `[4, 5, 6]` horizontally.\n", 102 | "\n", 103 | "### **Solution:**" 104 | ] 105 | }, 106 | { 107 | "cell_type": "code", 108 | "execution_count": 2, 109 | "metadata": {}, 110 | "outputs": [ 111 | { 112 | "name": "stdout", 113 | "output_type": "stream", 114 | "text": [ 115 | "[[1 2 3]\n", 116 | " [4 5 6]]\n", 117 | "[1 2 3 4 5 6]\n", 118 | "[1 2 3 4 5 6]\n" 119 | ] 120 | } 121 | ], 122 | "source": [ 123 | "# 1. Reshape an array\n", 124 | "arr_reshape = np.array([1, 2, 3, 4, 5, 6]).reshape(2, 3)\n", 125 | "\n", 126 | "# 2. Flatten a 2D array\n", 127 | "arr_2d = np.array([[1, 2, 3], [4, 5, 6]])\n", 128 | "arr_flatten = arr_2d.flatten()\n", 129 | "\n", 130 | "# 3. Concatenate arrays\n", 131 | "arr1 = np.array([1, 2, 3])\n", 132 | "arr2 = np.array([4, 5, 6])\n", 133 | "arr_concat = np.concatenate((arr1, arr2))\n", 134 | "\n", 135 | "print(arr_reshape)\n", 136 | "print(arr_flatten)\n", 137 | "print(arr_concat)" 138 | ] 139 | }, 140 | { 141 | "cell_type": "markdown", 142 | "metadata": {}, 143 | "source": [ 144 | "## Assignment 3: Array Broadcasting\n", 145 | "\n", 146 | "### **Concept:**\n", 147 | "Practice broadcasting rules in NumPy for element-wise operations.\n", 148 | "\n", 149 | "### **Tasks:**\n", 150 | "\n", 151 | "1. **Add a scalar to an array:** \n", 152 | " Add `10` to each element of the array `[1, 2, 3, 4]`.\n", 153 | "\n", 154 | "2. **Add a 1D array to a 2D array:** \n", 155 | " Add `[1, 2, 3]` to each row of the 2D array: \n", 156 | " ```\n", 157 | " [[10, 20, 30],\n", 158 | " [40, 50, 60]]\n", 159 | " ```\n", 160 | "\n", 161 | "### **Solution:**" 162 | ] 163 | }, 164 | { 165 | "cell_type": "code", 166 | "execution_count": null, 167 | "metadata": {}, 168 | "outputs": [], 169 | "source": [ 170 | "# 1. Add a scalar to an array\n", 171 | "arr = np.array([1, 2, 3, 4])\n", 172 | "arr_scalar = arr + 10\n", 173 | "\n", 174 | "# 2. Add a 1D array to a 2D array\n", 175 | "arr_2d = np.array([[10, 20, 30], [40, 50, 60]])\n", 176 | "arr_1d = np.array([1, 2, 3])\n", 177 | "arr_broadcast = arr_2d + arr_1d\n", 178 | "\n", 179 | "print(arr_scalar)\n", 180 | "print(arr_broadcast)" 181 | ] 182 | }, 183 | { 184 | "cell_type": "markdown", 185 | "metadata": {}, 186 | "source": [ 187 | "## Assignment 4: Mathematical Operations\n", 188 | "\n", 189 | "### **Concept:**\n", 190 | "Perform mathematical operations on NumPy arrays.\n", 191 | "\n", 192 | "### **Tasks:**\n", 193 | "\n", 194 | "1. **Element-wise operations:** \n", 195 | " Given `arr1 = [1, 2, 3]` and `arr2 = [4, 5, 6]`, compute the following:\n", 196 | " - Sum of `arr1` and `arr2`\n", 197 | " - Product of `arr1` and `arr2`\n", 198 | "\n", 199 | "2. **Compute mean and standard deviation:** \n", 200 | " Calculate the mean and standard deviation of the array `[2, 4, 6, 8, 10]`.\n", 201 | "\n", 202 | "### **Solution:**" 203 | ] 204 | }, 205 | { 206 | "cell_type": "code", 207 | "execution_count": null, 208 | "metadata": {}, 209 | "outputs": [], 210 | "source": [ 211 | "# 1. Element-wise operations\n", 212 | "arr1 = np.array([1, 2, 3])\n", 213 | "arr2 = np.array([4, 5, 6])\n", 214 | "\n", 215 | "arr_sum = arr1 + arr2\n", 216 | "arr_product = arr1 * arr2\n", 217 | "\n", 218 | "# 2. Compute mean and standard deviation\n", 219 | "arr = np.array([2, 4, 6, 8, 10])\n", 220 | "mean = np.mean(arr)\n", 221 | "std_dev = np.std(arr)\n", 222 | "\n", 223 | "print(arr_sum)\n", 224 | "print(arr_product)\n", 225 | "print(f\"Mean: {mean}, Standard Deviation: {std_dev}\")" 226 | ] 227 | }, 228 | { 229 | "cell_type": "markdown", 230 | "metadata": {}, 231 | "source": [ 232 | "## Assignment 5: Indexing and Slicing\n", 233 | "\n", 234 | "### **Concept:**\n", 235 | "Practice advanced indexing and slicing techniques in NumPy.\n", 236 | "\n", 237 | "### **Tasks:**\n", 238 | "\n", 239 | "1. **Indexing:** \n", 240 | " Extract the element at position `(1, 2)` from the following array: \n", 241 | " ```\n", 242 | " [[10, 20, 30],\n", 243 | " [40, 50, 60],\n", 244 | " [70, 80, 90]]\n", 245 | " ```\n", 246 | "\n", 247 | "2. **Slicing:** \n", 248 | " Extract the second row and first two columns from the array above.\n", 249 | "\n", 250 | "3. **Boolean indexing:** \n", 251 | " Given `arr = [5, 10, 15, 20]`, extract elements greater than `10`.\n", 252 | "\n", 253 | "### **Solution:**" 254 | ] 255 | }, 256 | { 257 | "cell_type": "code", 258 | "execution_count": null, 259 | "metadata": {}, 260 | "outputs": [], 261 | "source": [ 262 | "# Given array\n", 263 | "arr_2d = np.array([[10, 20, 30], [40, 50, 60], [70, 80, 90]])\n", 264 | "\n", 265 | "# 1. Indexing\n", 266 | "element = arr_2d[1, 2]\n", 267 | "\n", 268 | "# 2. Slicing\n", 269 | "slice_part = arr_2d[1, :2]\n", 270 | "\n", 271 | "# 3. Boolean indexing\n", 272 | "arr = np.array([5, 10, 15, 20])\n", 273 | "filtered = arr[arr > 10]\n", 274 | "\n", 275 | "print(element)\n", 276 | "print(slice_part)\n", 277 | "print(filtered)" 278 | ] 279 | }, 280 | { 281 | "cell_type": "markdown", 282 | "metadata": {}, 283 | "source": [ 284 | "## Assignment 6: Common Functions\n", 285 | "\n", 286 | "### **Concept:**\n", 287 | "Explore common NumPy functions such as `sum`, `max`, `min`, and `argmax`.\n", 288 | "\n", 289 | "### **Tasks:**\n", 290 | "\n", 291 | "1. **Compute sum and max:** \n", 292 | " Given the array `[3, 7, 2, 9, 5]`, compute the sum and maximum value.\n", 293 | "\n", 294 | "2. **Find the index of the maximum value:** \n", 295 | " Find the index of the maximum value in the same array.\n", 296 | "\n", 297 | "### **Solution:**" 298 | ] 299 | }, 300 | { 301 | "cell_type": "code", 302 | "execution_count": null, 303 | "metadata": {}, 304 | "outputs": [], 305 | "source": [ 306 | "arr = np.array([3, 7, 2, 9, 5])\n", 307 | "\n", 308 | "# Sum and max\n", 309 | "total = np.sum(arr)\n", 310 | "maximum = np.max(arr)\n", 311 | "\n", 312 | "# Index of max\n", 313 | "index_max = np.argmax(arr)\n", 314 | "\n", 315 | "print(f\"Sum: {total}, Max: {maximum}, Index of Max: {index_max}\")" 316 | ] 317 | }, 318 | { 319 | "cell_type": "markdown", 320 | "metadata": {}, 321 | "source": [ 322 | "## Assignment 7: Array Creation with Random Numbers\n", 323 | "\n", 324 | "### **Concept:**\n", 325 | "Creating arrays using random number generation.\n", 326 | "\n", 327 | "### **Tasks:**\n", 328 | "\n", 329 | "1. **Generate a random 3x3 array of floats between 0 and 1.**\n", 330 | "\n", 331 | "2. **Create a 4x4 array of random integers between 10 and 50.**\n", 332 | "\n", 333 | "3. **Set a random seed to make the random numbers reproducible. Generate a 2x3 array of random numbers.**\n", 334 | "\n", 335 | "### **Solution:**" 336 | ] 337 | }, 338 | { 339 | "cell_type": "code", 340 | "execution_count": null, 341 | "metadata": {}, 342 | "outputs": [], 343 | "source": [ 344 | "import numpy as np\n", 345 | "\n", 346 | "# 1. Random 3x3 array of floats\n", 347 | "random_floats = np.random.rand(3, 3)\n", 348 | "\n", 349 | "# 2. Random 4x4 array of integers between 10 and 50\n", 350 | "random_integers = np.random.randint(10, 50, size=(4, 4))\n", 351 | "\n", 352 | "# 3. Reproducible random numbers with a seed\n", 353 | "np.random.seed(42)\n", 354 | "random_seeded = np.random.rand(2, 3)\n", 355 | "\n", 356 | "print(random_floats)\n", 357 | "print(random_integers)\n", 358 | "print(random_seeded)" 359 | ] 360 | }, 361 | { 362 | "cell_type": "markdown", 363 | "metadata": {}, 364 | "source": [ 365 | "## Assignment 8: Advanced Array Manipulation\n", 366 | "\n", 367 | "### **Concept:**\n", 368 | "Stacking and splitting arrays.\n", 369 | "\n", 370 | "### **Tasks:**\n", 371 | "\n", 372 | "1. **Vertically stack the following arrays:** \n", 373 | " ```\n", 374 | " arr1 = [[1, 2], [3, 4]]\n", 375 | " arr2 = [[5, 6], [7, 8]]\n", 376 | " ```\n", 377 | "\n", 378 | "2. **Horizontally stack the same arrays.**\n", 379 | "\n", 380 | "3. **Split the following array into 3 equal parts along the first axis:** \n", 381 | " ```\n", 382 | " arr = [[1, 2], [3, 4], [5, 6]]\n", 383 | " ```\n", 384 | "\n", 385 | "### **Solution:**" 386 | ] 387 | }, 388 | { 389 | "cell_type": "code", 390 | "execution_count": null, 391 | "metadata": {}, 392 | "outputs": [], 393 | "source": [ 394 | "arr1 = np.array([[1, 2], [3, 4]])\n", 395 | "arr2 = np.array([[5, 6], [7, 8]])\n", 396 | "\n", 397 | "# 1. Vertical stacking\n", 398 | "vstack_result = np.vstack((arr1, arr2))\n", 399 | "\n", 400 | "# 2. Horizontal stacking\n", 401 | "hstack_result = np.hstack((arr1, arr2))\n", 402 | "\n", 403 | "# 3. Splitting an array\n", 404 | "arr = np.array([[1, 2], [3, 4], [5, 6]])\n", 405 | "split_result = np.array_split(arr, 3)\n", 406 | "\n", 407 | "print(vstack_result)\n", 408 | "print(hstack_result)\n", 409 | "print(split_result)" 410 | ] 411 | }, 412 | { 413 | "cell_type": "markdown", 414 | "metadata": {}, 415 | "source": [ 416 | "## Assignment 9: Broadcasting and Advanced Arithmetic\n", 417 | "\n", 418 | "### **Concept:**\n", 419 | "Performing broadcasting with arithmetic operations.\n", 420 | "\n", 421 | "### **Tasks:**\n", 422 | "\n", 423 | "1. **Broadcast the following 1D array to a 3x3 array and add:** \n", 424 | " ```\n", 425 | " base = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]\n", 426 | " add_array = [10, 20, 30]\n", 427 | " ```\n", 428 | "\n", 429 | "2. **Multiply a 3x3 matrix by a scalar value of 2.**\n", 430 | "\n", 431 | "3. **Divide each row of the following matrix by a different value:** \n", 432 | " ```\n", 433 | " arr = [[10, 20, 30],\n", 434 | " [40, 50, 60],\n", 435 | " [70, 80, 90]]\n", 436 | " divisors = [10, 20, 30]\n", 437 | " ```\n", 438 | "\n", 439 | "### **Solution:**" 440 | ] 441 | }, 442 | { 443 | "cell_type": "code", 444 | "execution_count": null, 445 | "metadata": {}, 446 | "outputs": [], 447 | "source": [ 448 | "# 1. Broadcasting addition\n", 449 | "base = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])\n", 450 | "add_array = np.array([10, 20, 30])\n", 451 | "result_add = base + add_array\n", 452 | "\n", 453 | "# 2. Scalar multiplication\n", 454 | "result_mult = base * 2\n", 455 | "\n", 456 | "# 3. Row-wise division\n", 457 | "arr = np.array([[10, 20, 30], [40, 50, 60], [70, 80, 90]])\n", 458 | "divisors = np.array([10, 20, 30]).reshape(3, 1)\n", 459 | "result_div = arr / divisors\n", 460 | "\n", 461 | "print(result_add)\n", 462 | "print(result_mult)\n", 463 | "print(result_div)" 464 | ] 465 | }, 466 | { 467 | "cell_type": "markdown", 468 | "metadata": {}, 469 | "source": [ 470 | "## Assignment 10: Indexing, Slicing, and Fancy Indexing\n", 471 | "\n", 472 | "### **Concept:**\n", 473 | "Practice with indexing techniques and fancy indexing.\n", 474 | "\n", 475 | "### **Tasks:**\n", 476 | "\n", 477 | "1. **Extract all even numbers from the following array:** \n", 478 | " ```\n", 479 | " arr = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n", 480 | " ```\n", 481 | "\n", 482 | "2. **Using fancy indexing, select the elements at positions `[0, 2, 4]` from the array:** \n", 483 | " ```\n", 484 | " arr = [10, 20, 30, 40, 50]\n", 485 | " ```\n", 486 | "\n", 487 | "3. **Extract the second and third rows and the first and second columns from this 2D array:** \n", 488 | " ```\n", 489 | " arr = [[1, 2, 3],\n", 490 | " [4, 5, 6],\n", 491 | " [7, 8, 9],\n", 492 | " [10, 11, 12]]\n", 493 | " ```\n", 494 | "\n", 495 | "### **Solution:**" 496 | ] 497 | }, 498 | { 499 | "cell_type": "code", 500 | "execution_count": null, 501 | "metadata": {}, 502 | "outputs": [], 503 | "source": [ 504 | "# 1. Extract even numbers\n", 505 | "arr = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])\n", 506 | "even_numbers = arr[arr % 2 == 0]\n", 507 | "\n", 508 | "# 2. Fancy indexing\n", 509 | "arr2 = np.array([10, 20, 30, 40, 50])\n", 510 | "fancy_selected = arr2[[0, 2, 4]]\n", 511 | "\n", 512 | "# 3. Extract specific rows and columns\n", 513 | "arr3 = np.array([[1, 2, 3],\n", 514 | " [4, 5, 6],\n", 515 | " [7, 8, 9],\n", 516 | " [10, 11, 12]])\n", 517 | "sliced_part = arr3[1:3, 0:2]\n", 518 | "\n", 519 | "print(even_numbers)\n", 520 | "print(fancy_selected)\n", 521 | "print(sliced_part)" 522 | ] 523 | }, 524 | { 525 | "cell_type": "markdown", 526 | "metadata": {}, 527 | "source": [ 528 | "## Assignment 11: Common Functions and Statistics\n", 529 | "\n", 530 | "### **Concept:**\n", 531 | "Using NumPy's built-in statistical functions.\n", 532 | "\n", 533 | "### **Tasks:**\n", 534 | "\n", 535 | "1. **Find the minimum, maximum, and sum of the following array:** \n", 536 | " ```\n", 537 | " arr = [3, 7, 1, 9, 5]\n", 538 | " ```\n", 539 | "\n", 540 | "2. **Compute the mean, median, and standard deviation of the following array:** \n", 541 | " ```\n", 542 | " arr = [10, 20, 30, 40, 50]\n", 543 | " ```\n", 544 | "\n", 545 | "3. **Find the indices of the minimum and maximum values in the array:** \n", 546 | " ```\n", 547 | " arr = [4, 2, 9, 1, 6]\n", 548 | " ```\n", 549 | "\n", 550 | "### **Solution:**" 551 | ] 552 | }, 553 | { 554 | "cell_type": "code", 555 | "execution_count": null, 556 | "metadata": {}, 557 | "outputs": [], 558 | "source": [ 559 | "# 1. Min, max, and sum\n", 560 | "arr1 = np.array([3, 7, 1, 9, 5])\n", 561 | "min_val = np.min(arr1)\n", 562 | "max_val = np.max(arr1)\n", 563 | "total_sum = np.sum(arr1)\n", 564 | "\n", 565 | "# 2. Mean, median, and std\n", 566 | "arr2 = np.array([10, 20, 30, 40, 50])\n", 567 | "mean_val = np.mean(arr2)\n", 568 | "median_val = np.median(arr2)\n", 569 | "std_val = np.std(arr2)\n", 570 | "\n", 571 | "# 3. Indices of min and max\n", 572 | "arr3 = np.array([4, 2, 9, 1, 6])\n", 573 | "index_min = np.argmin(arr3)\n", 574 | "index_max = np.argmax(arr3)\n", 575 | "\n", 576 | "print(f\"Min: {min_val}, Max: {max_val}, Sum: {total_sum}\")\n", 577 | "print(f\"Mean: {mean_val}, Median: {median_val}, Std Dev: {std_val}\")\n", 578 | "print(f\"Index of Min: {index_min}, Index of Max: {index_max}\")" 579 | ] 580 | }, 581 | { 582 | "cell_type": "code", 583 | "execution_count": null, 584 | "metadata": {}, 585 | "outputs": [], 586 | "source": [] 587 | } 588 | ], 589 | "metadata": { 590 | "kernelspec": { 591 | "display_name": "pytorch23", 592 | "language": "python", 593 | "name": "python3" 594 | }, 595 | "language_info": { 596 | "codemirror_mode": { 597 | "name": "ipython", 598 | "version": 3 599 | }, 600 | "file_extension": ".py", 601 | "mimetype": "text/x-python", 602 | "name": "python", 603 | "nbconvert_exporter": "python", 604 | "pygments_lexer": "ipython3", 605 | "version": "3.9.19" 606 | } 607 | }, 608 | "nbformat": 4, 609 | "nbformat_minor": 2 610 | } 611 | -------------------------------------------------------------------------------- /01_Introductory_Concepts/04_Numpy/NumPy_Assignments/NumPy_Assignment_Template.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# 📝 **NumPy Assignment Series**" 8 | ] 9 | }, 10 | { 11 | "cell_type": "markdown", 12 | "metadata": {}, 13 | "source": [ 14 | "## Assignment 1: Array Creation and Initialization\n", 15 | "\n", 16 | "### **Concept:**\n", 17 | "Learn how to create and initialize NumPy arrays using different methods.\n", 18 | "\n", 19 | "### **Tasks:**\n", 20 | "\n", 21 | "1. **Create an array from a list:** \n", 22 | " Convert the following list into a NumPy array: `[1, 2, 3, 4, 5]`.\n", 23 | "\n", 24 | "2. **Create arrays using functions:** \n", 25 | " - Create an array of zeros with shape `(3, 3)`.\n", 26 | " - Create an array of ones with shape `(2, 4)`.\n", 27 | " - Create an array of evenly spaced values between 0 and 10 with a step of 2.\n", 28 | "\n", 29 | "3. **Create an identity matrix:** \n", 30 | " Create a 4x4 identity matrix.\n", 31 | "\n", 32 | "### **Solution:**" 33 | ] 34 | }, 35 | { 36 | "cell_type": "code", 37 | "execution_count": null, 38 | "metadata": {}, 39 | "outputs": [], 40 | "source": [] 41 | }, 42 | { 43 | "cell_type": "markdown", 44 | "metadata": {}, 45 | "source": [ 46 | "## Assignment 2: Array Manipulation\n", 47 | "\n", 48 | "### **Concept:**\n", 49 | "Learn how to reshape, flatten, and concatenate arrays.\n", 50 | "\n", 51 | "### **Tasks:**\n", 52 | "\n", 53 | "1. **Reshape an array:** \n", 54 | " Reshape the array `[1, 2, 3, 4, 5, 6]` into a 2x3 matrix.\n", 55 | "\n", 56 | "2. **Flatten a 2D array:** \n", 57 | " Flatten the following 2D array: \n", 58 | " ```\n", 59 | " [[1, 2, 3],\n", 60 | " [4, 5, 6]]\n", 61 | " ```\n", 62 | "\n", 63 | "3. **Concatenate arrays:** \n", 64 | " Concatenate the arrays `[1, 2, 3]` and `[4, 5, 6]` horizontally.\n", 65 | "\n", 66 | "### **Solution:**" 67 | ] 68 | }, 69 | { 70 | "cell_type": "code", 71 | "execution_count": null, 72 | "metadata": {}, 73 | "outputs": [], 74 | "source": [] 75 | }, 76 | { 77 | "cell_type": "markdown", 78 | "metadata": {}, 79 | "source": [ 80 | "## Assignment 3: Array Broadcasting\n", 81 | "\n", 82 | "### **Concept:**\n", 83 | "Practice broadcasting rules in NumPy for element-wise operations.\n", 84 | "\n", 85 | "### **Tasks:**\n", 86 | "\n", 87 | "1. **Add a scalar to an array:** \n", 88 | " Add `10` to each element of the array `[1, 2, 3, 4]`.\n", 89 | "\n", 90 | "2. **Add a 1D array to a 2D array:** \n", 91 | " Add `[1, 2, 3]` to each row of the 2D array: \n", 92 | " ```\n", 93 | " [[10, 20, 30],\n", 94 | " [40, 50, 60]]\n", 95 | " ```\n", 96 | "\n", 97 | "### **Solution:**" 98 | ] 99 | }, 100 | { 101 | "cell_type": "code", 102 | "execution_count": 1, 103 | "metadata": {}, 104 | "outputs": [ 105 | { 106 | "name": "stdout", 107 | "output_type": "stream", 108 | "text": [ 109 | "3D Matrix:\n", 110 | "[[[ 1 2 3 4]\n", 111 | " [ 5 6 7 8]\n", 112 | " [ 9 10 11 12]]\n", 113 | "\n", 114 | " [[13 14 15 16]\n", 115 | " [17 18 19 20]\n", 116 | " [21 22 23 24]]]\n", 117 | "\n", 118 | "1D Vector:\n", 119 | "[10 20 30 40]\n", 120 | "\n", 121 | "Result after broadcasting addition:\n", 122 | "[[[11 22 33 44]\n", 123 | " [15 26 37 48]\n", 124 | " [19 30 41 52]]\n", 125 | "\n", 126 | " [[23 34 45 56]\n", 127 | " [27 38 49 60]\n", 128 | " [31 42 53 64]]]\n" 129 | ] 130 | } 131 | ], 132 | "source": [ 133 | "import numpy as np \n", 134 | "\n", 135 | "# Create a 3D matrix (shape: 2x3x4) \n", 136 | "matrix_3d = np.array([[[1, 2, 3, 4], \n", 137 | " [5, 6, 7, 8], \n", 138 | " [9, 10, 11, 12]], \n", 139 | " \n", 140 | " [[13, 14, 15, 16], \n", 141 | " [17, 18, 19, 20], \n", 142 | " [21, 22, 23, 24]]]) \n", 143 | "\n", 144 | "# Create a 1D vector (shape: 4) \n", 145 | "vector_1d = np.array([10, 20, 30, 40]) \n", 146 | "\n", 147 | "# Add the 1D vector to the 3D matrix using broadcasting \n", 148 | "result = matrix_3d + vector_1d \n", 149 | "\n", 150 | "print(\"3D Matrix:\") \n", 151 | "print(matrix_3d) \n", 152 | "print(\"\\n1D Vector:\") \n", 153 | "print(vector_1d) \n", 154 | "print(\"\\nResult after broadcasting addition:\") \n", 155 | "print(result)" 156 | ] 157 | }, 158 | { 159 | "cell_type": "markdown", 160 | "metadata": {}, 161 | "source": [ 162 | "## Assignment 4: Mathematical Operations\n", 163 | "\n", 164 | "### **Concept:**\n", 165 | "Perform mathematical operations on NumPy arrays.\n", 166 | "\n", 167 | "### **Tasks:**\n", 168 | "\n", 169 | "1. **Element-wise operations:** \n", 170 | " Given `arr1 = [1, 2, 3]` and `arr2 = [4, 5, 6]`, compute the following:\n", 171 | " - Sum of `arr1` and `arr2`\n", 172 | " - Product of `arr1` and `arr2`\n", 173 | "\n", 174 | "2. **Compute mean and standard deviation:** \n", 175 | " Calculate the mean and standard deviation of the array `[2, 4, 6, 8, 10]`.\n", 176 | "\n", 177 | "### **Solution:**" 178 | ] 179 | }, 180 | { 181 | "cell_type": "code", 182 | "execution_count": null, 183 | "metadata": {}, 184 | "outputs": [], 185 | "source": [] 186 | }, 187 | { 188 | "cell_type": "markdown", 189 | "metadata": {}, 190 | "source": [ 191 | "## Assignment 5: Indexing and Slicing\n", 192 | "\n", 193 | "### **Concept:**\n", 194 | "Practice advanced indexing and slicing techniques in NumPy.\n", 195 | "\n", 196 | "### **Tasks:**\n", 197 | "\n", 198 | "1. **Indexing:** \n", 199 | " Extract the element at position `(1, 2)` from the following array: \n", 200 | " ```\n", 201 | " [[10, 20, 30],\n", 202 | " [40, 50, 60],\n", 203 | " [70, 80, 90]]\n", 204 | " ```\n", 205 | "\n", 206 | "2. **Slicing:** \n", 207 | " Extract the second row and first two columns from the array above.\n", 208 | "\n", 209 | "3. **Boolean indexing:** \n", 210 | " Given `arr = [5, 10, 15, 20]`, extract elements greater than `10`.\n", 211 | "\n", 212 | "### **Solution:**" 213 | ] 214 | }, 215 | { 216 | "cell_type": "code", 217 | "execution_count": null, 218 | "metadata": {}, 219 | "outputs": [], 220 | "source": [] 221 | }, 222 | { 223 | "cell_type": "markdown", 224 | "metadata": {}, 225 | "source": [ 226 | "## Assignment 6: Common Functions\n", 227 | "\n", 228 | "### **Concept:**\n", 229 | "Explore common NumPy functions such as `sum`, `max`, `min`, and `argmax`.\n", 230 | "\n", 231 | "### **Tasks:**\n", 232 | "\n", 233 | "1. **Compute sum and max:** \n", 234 | " Given the array `[3, 7, 2, 9, 5]`, compute the sum and maximum value.\n", 235 | "\n", 236 | "2. **Find the index of the maximum value:** \n", 237 | " Find the index of the maximum value in the same array.\n", 238 | "\n", 239 | "### **Solution:**" 240 | ] 241 | }, 242 | { 243 | "cell_type": "code", 244 | "execution_count": null, 245 | "metadata": {}, 246 | "outputs": [], 247 | "source": [] 248 | }, 249 | { 250 | "cell_type": "markdown", 251 | "metadata": {}, 252 | "source": [ 253 | "## Assignment 7: Array Creation with Random Numbers\n", 254 | "\n", 255 | "### **Concept:**\n", 256 | "Creating arrays using random number generation.\n", 257 | "\n", 258 | "### **Tasks:**\n", 259 | "\n", 260 | "1. **Generate a random 3x3 array of floats between 0 and 1.**\n", 261 | "\n", 262 | "2. **Create a 4x4 array of random integers between 10 and 50.**\n", 263 | "\n", 264 | "3. **Set a random seed to make the random numbers reproducible. Generate a 2x3 array of random numbers.**\n", 265 | "\n", 266 | "### **Solution:**" 267 | ] 268 | }, 269 | { 270 | "cell_type": "code", 271 | "execution_count": null, 272 | "metadata": {}, 273 | "outputs": [], 274 | "source": [] 275 | }, 276 | { 277 | "cell_type": "markdown", 278 | "metadata": {}, 279 | "source": [ 280 | "## Assignment 8: Advanced Array Manipulation\n", 281 | "\n", 282 | "### **Concept:**\n", 283 | "Stacking and splitting arrays.\n", 284 | "\n", 285 | "### **Tasks:**\n", 286 | "\n", 287 | "1. **Vertically stack the following arrays:** \n", 288 | " ```\n", 289 | " arr1 = [[1, 2], [3, 4]]\n", 290 | " arr2 = [[5, 6], [7, 8]]\n", 291 | " ```\n", 292 | "\n", 293 | "2. **Horizontally stack the same arrays.**\n", 294 | "\n", 295 | "3. **Split the following array into 3 equal parts along the first axis:** \n", 296 | " ```\n", 297 | " arr = [[1, 2], [3, 4], [5, 6]]\n", 298 | " ```\n", 299 | "\n", 300 | "### **Solution:**" 301 | ] 302 | }, 303 | { 304 | "cell_type": "code", 305 | "execution_count": null, 306 | "metadata": {}, 307 | "outputs": [], 308 | "source": [] 309 | }, 310 | { 311 | "cell_type": "markdown", 312 | "metadata": {}, 313 | "source": [ 314 | "## Assignment 9: Broadcasting and Advanced Arithmetic\n", 315 | "\n", 316 | "### **Concept:**\n", 317 | "Performing broadcasting with arithmetic operations.\n", 318 | "\n", 319 | "### **Tasks:**\n", 320 | "\n", 321 | "1. **Broadcast the following 1D array to a 3x3 array and add:** \n", 322 | " ```\n", 323 | " base = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]\n", 324 | " add_array = [10, 20, 30]\n", 325 | " ```\n", 326 | "\n", 327 | "2. **Multiply a 3x3 matrix by a scalar value of 2.**\n", 328 | "\n", 329 | "3. **Divide each row of the following matrix by a different value:** \n", 330 | " ```\n", 331 | " arr = [[10, 20, 30],\n", 332 | " [40, 50, 60],\n", 333 | " [70, 80, 90]]\n", 334 | " divisors = [10, 20, 30]\n", 335 | " ```\n", 336 | "\n", 337 | "### **Solution:**" 338 | ] 339 | }, 340 | { 341 | "cell_type": "code", 342 | "execution_count": null, 343 | "metadata": {}, 344 | "outputs": [], 345 | "source": [] 346 | }, 347 | { 348 | "cell_type": "markdown", 349 | "metadata": {}, 350 | "source": [ 351 | "## Assignment 10: Indexing, Slicing, and Fancy Indexing\n", 352 | "\n", 353 | "### **Concept:**\n", 354 | "Practice with indexing techniques and fancy indexing.\n", 355 | "\n", 356 | "### **Tasks:**\n", 357 | "\n", 358 | "1. **Extract all even numbers from the following array:** \n", 359 | " ```\n", 360 | " arr = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n", 361 | " ```\n", 362 | "\n", 363 | "2. **Using fancy indexing, select the elements at positions `[0, 2, 4]` from the array:** \n", 364 | " ```\n", 365 | " arr = [10, 20, 30, 40, 50]\n", 366 | " ```\n", 367 | "\n", 368 | "3. **Extract the second and third rows and the first and second columns from this 2D array:** \n", 369 | " ```\n", 370 | " arr = [[1, 2, 3],\n", 371 | " [4, 5, 6],\n", 372 | " [7, 8, 9],\n", 373 | " [10, 11, 12]]\n", 374 | " ```\n", 375 | "\n", 376 | "### **Solution:**" 377 | ] 378 | }, 379 | { 380 | "cell_type": "code", 381 | "execution_count": null, 382 | "metadata": {}, 383 | "outputs": [], 384 | "source": [] 385 | }, 386 | { 387 | "cell_type": "markdown", 388 | "metadata": {}, 389 | "source": [ 390 | "## Assignment 11: Common Functions and Statistics\n", 391 | "\n", 392 | "### **Concept:**\n", 393 | "Using NumPy's built-in statistical functions.\n", 394 | "\n", 395 | "### **Tasks:**\n", 396 | "\n", 397 | "1. **Find the minimum, maximum, and sum of the following array:** \n", 398 | " ```\n", 399 | " arr = [3, 7, 1, 9, 5]\n", 400 | " ```\n", 401 | "\n", 402 | "2. **Compute the mean, median, and standard deviation of the following array:** \n", 403 | " ```\n", 404 | " arr = [10, 20, 30, 40, 50]\n", 405 | " ```\n", 406 | "\n", 407 | "3. **Find the indices of the minimum and maximum values in the array:** \n", 408 | " ```\n", 409 | " arr = [4, 2, 9, 1, 6]\n", 410 | " ```\n", 411 | "\n", 412 | "### **Solution:**" 413 | ] 414 | }, 415 | { 416 | "cell_type": "code", 417 | "execution_count": null, 418 | "metadata": {}, 419 | "outputs": [], 420 | "source": [] 421 | } 422 | ], 423 | "metadata": { 424 | "kernelspec": { 425 | "display_name": "pytorch23", 426 | "language": "python", 427 | "name": "python3" 428 | }, 429 | "language_info": { 430 | "codemirror_mode": { 431 | "name": "ipython", 432 | "version": 3 433 | }, 434 | "file_extension": ".py", 435 | "mimetype": "text/x-python", 436 | "name": "python", 437 | "nbconvert_exporter": "python", 438 | "pygments_lexer": "ipython3", 439 | "version": "3.9.19" 440 | } 441 | }, 442 | "nbformat": 4, 443 | "nbformat_minor": 2 444 | } 445 | -------------------------------------------------------------------------------- /01_Introductory_Concepts/imgs/ML.png: -------------------------------------------------------------------------------- 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KDTree_BallTree.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [], 3 | "metadata": { 4 | "language_info": { 5 | "name": "python" 6 | } 7 | }, 8 | "nbformat": 4, 9 | "nbformat_minor": 2 10 | } 11 | -------------------------------------------------------------------------------- /Before codes/03_Supervised_Learning/18. Random Forest.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [], 3 | "metadata": { 4 | "language_info": { 5 | "name": "python" 6 | } 7 | }, 8 | "nbformat": 4, 9 | "nbformat_minor": 2 10 | } 11 | -------------------------------------------------------------------------------- /Before codes/03_Supervised_Learning/Templates/Template_06. Univariate_linear_regression.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "id": "b46dfaac", 6 | "metadata": {}, 7 | "source": [ 8 | "# Example 1: Advertising Data" 9 | ] 10 | }, 11 | { 12 | "cell_type": "markdown", 13 | "id": "34cbbbd1", 14 | "metadata": {}, 15 | "source": [ 16 | "![alt text](https://raw.githubusercontent.com/Ebimsv/Machine_Learning_Course/refs/heads/main/pics/adv.png)" 17 | ] 18 | }, 19 | { 20 | "cell_type": "markdown", 21 | "id": "02e9e101", 22 | "metadata": {}, 23 | "source": [ 24 | "This dataset contains information on advertising expenditures across three media channels—TV, Radio, and Newspaper—and their corresponding sales figures. Each row represents a unique observation, including both the financial investment in advertising and the resulting sales performance. \n", 25 | "\n", 26 | "The columns are defined as follows:\n", 27 | "\n", 28 | "- **TV**: Advertising spend in thousands of dollars on TV.\n", 29 | "- **Radio**: Advertising spend in thousands of dollars on Radio.\n", 30 | "- **Newspaper**: Advertising spend in thousands of dollars on Newspaper.\n", 31 | "- **Sales**: The number of units sold, measured in thousands.\n", 32 | "\n", 33 | "For instance, the first entry indicates that spending $230.1K on TV, $37.8K on Radio, and $69.2K on Newspaper resulted in sales of 22.1K units." 34 | ] 35 | }, 36 | { 37 | "cell_type": "markdown", 38 | "id": "eb0aaa48-ea27-43ea-8f49-1c120af69ae1", 39 | "metadata": { 40 | "jp-MarkdownHeadingCollapsed": true, 41 | "tags": [] 42 | }, 43 | "source": [ 44 | "## imports" 45 | ] 46 | }, 47 | { 48 | "cell_type": "code", 49 | "execution_count": 5, 50 | "id": "6cfc7dfd-b79f-42ed-a12e-11a6dc14beda", 51 | "metadata": {}, 52 | "outputs": [], 53 | "source": [ 54 | "import pandas as pd\n", 55 | "import numpy as np\n", 56 | "import matplotlib.pyplot as plt" 57 | ] 58 | }, 59 | { 60 | "cell_type": "markdown", 61 | "id": "8cbd0742-4327-4dca-9d9c-03c01086ccb4", 62 | "metadata": { 63 | "tags": [] 64 | }, 65 | "source": [ 66 | "## Dataset" 67 | ] 68 | }, 69 | { 70 | "cell_type": "code", 71 | "execution_count": 7, 72 | "id": "0c4da9d5-1593-42c8-a1e6-e777345d42b2", 73 | "metadata": {}, 74 | "outputs": [ 75 | { 76 | "data": { 77 | "text/html": [ 78 | "
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Unnamed: 0TVRadioNewspaperSales
01230.137.869.222.1
1244.539.345.110.4
2317.245.969.39.3
34151.541.358.518.5
45180.810.858.412.9
\n", 146 | "
" 147 | ], 148 | "text/plain": [ 149 | " Unnamed: 0 TV Radio Newspaper Sales\n", 150 | "0 1 230.1 37.8 69.2 22.1\n", 151 | "1 2 44.5 39.3 45.1 10.4\n", 152 | "2 3 17.2 45.9 69.3 9.3\n", 153 | "3 4 151.5 41.3 58.5 18.5\n", 154 | "4 5 180.8 10.8 58.4 12.9" 155 | ] 156 | }, 157 | "execution_count": 7, 158 | "metadata": {}, 159 | "output_type": "execute_result" 160 | } 161 | ], 162 | "source": [ 163 | "df = pd.read_csv('../../Data/Advertising.csv')\n", 164 | "df.head()" 165 | ] 166 | }, 167 | { 168 | "cell_type": "markdown", 169 | "id": "65d194c5-08ae-48fd-b5bb-49a095e1e89a", 170 | "metadata": {}, 171 | "source": [ 172 | "Advertising dataset contains information about the sales of a product in different markets, along with the advertising budget for the product in each market. \n", 173 | "The dataset includes 200 instances with **3 features**, such as the TV advertising budget, the radio advertising budget, and the newspaper advertising budget.\n", 174 | "\n", 175 | "The target variable is the sales of the product, which is also a **continuous** variable." 176 | ] 177 | }, 178 | { 179 | "cell_type": "markdown", 180 | "id": "279cd4b3-e7fa-4fa1-bcef-d4d8834226a3", 181 | "metadata": {}, 182 | "source": [ 183 | "## Select two features (columns) from a DataFrame" 184 | ] 185 | }, 186 | { 187 | "cell_type": "markdown", 188 | "id": "d4bcbe64", 189 | "metadata": {}, 190 | "source": [ 191 | "### Method 1: Using Double Brackets \n", 192 | "\n", 193 | "- You can select multiple columns from a DataFrame by passing a list of column names within double brackets. " 194 | ] 195 | }, 196 | { 197 | "cell_type": "code", 198 | "execution_count": null, 199 | "id": "62766871-c615-4f56-825b-34626b7e3617", 200 | "metadata": {}, 201 | "outputs": [], 202 | "source": [] 203 | }, 204 | { 205 | "cell_type": "markdown", 206 | "id": "5407e9be", 207 | "metadata": {}, 208 | "source": [ 209 | "### Method 2: Using `iloc`\n", 210 | "- You can also select features based on their integer index positions using `iloc`. \n", 211 | "- This method is particularly useful when you want to select columns at specific intervals or ranges." 212 | ] 213 | }, 214 | { 215 | "cell_type": "code", 216 | "execution_count": null, 217 | "id": "34884f6d-7915-4202-9014-5442b26a23d1", 218 | "metadata": {}, 219 | "outputs": [], 220 | "source": [] 221 | }, 222 | { 223 | "cell_type": "markdown", 224 | "id": "21b3208c", 225 | "metadata": {}, 226 | "source": [ 227 | "### Method 3: Using the filter Method\n", 228 | "- Another way to select multiple columns is by using the `filter()` method, which allows for more flexible selection options." 229 | ] 230 | }, 231 | { 232 | "cell_type": "code", 233 | "execution_count": null, 234 | "id": "65b296e9", 235 | "metadata": {}, 236 | "outputs": [], 237 | "source": [] 238 | }, 239 | { 240 | "cell_type": "code", 241 | "execution_count": null, 242 | "id": "db63e0cb-f23a-4b65-a2f6-a31ca00e3e41", 243 | "metadata": {}, 244 | "outputs": [], 245 | "source": [] 246 | }, 247 | { 248 | "cell_type": "markdown", 249 | "id": "080939fe-0930-4a7d-9f5f-73290b48d182", 250 | "metadata": {}, 251 | "source": [ 252 | "## Convert to NumPy array and then create `X`, and `y`" 253 | ] 254 | }, 255 | { 256 | "cell_type": "markdown", 257 | "id": "04fd722a", 258 | "metadata": {}, 259 | "source": [ 260 | "### **Method 1**: Using `to_numpy()`" 261 | ] 262 | }, 263 | { 264 | "cell_type": "code", 265 | "execution_count": null, 266 | "id": "1a16ad6e", 267 | "metadata": {}, 268 | "outputs": [], 269 | "source": [] 270 | }, 271 | { 272 | "cell_type": "markdown", 273 | "id": "6b5932f9", 274 | "metadata": {}, 275 | "source": [ 276 | "### **Method 2**: Using `values` Attribute" 277 | ] 278 | }, 279 | { 280 | "cell_type": "code", 281 | "execution_count": null, 282 | "id": "1a9cd4fe", 283 | "metadata": {}, 284 | "outputs": [], 285 | "source": [] 286 | }, 287 | { 288 | "cell_type": "markdown", 289 | "id": "2c45c2fb", 290 | "metadata": {}, 291 | "source": [ 292 | "### **Method 3**: Using `np.array()`" 293 | ] 294 | }, 295 | { 296 | "cell_type": "code", 297 | "execution_count": null, 298 | "id": "f4856042", 299 | "metadata": {}, 300 | "outputs": [], 301 | "source": [] 302 | }, 303 | { 304 | "cell_type": "markdown", 305 | "id": "079fb512", 306 | "metadata": {}, 307 | "source": [ 308 | "### **Method 4**: One-Line Selection and Reshape" 309 | ] 310 | }, 311 | { 312 | "cell_type": "code", 313 | "execution_count": null, 314 | "id": "9eea6fdf-e99f-471d-8a35-ab67623fc2cd", 315 | "metadata": {}, 316 | "outputs": [], 317 | "source": [] 318 | }, 319 | { 320 | "cell_type": "markdown", 321 | "id": "d33689b5", 322 | "metadata": {}, 323 | "source": [ 324 | "## Split the Data into Training and Testing Sets" 325 | ] 326 | }, 327 | { 328 | "cell_type": "code", 329 | "execution_count": null, 330 | "id": "6c09a55c-8d84-4f35-a686-f6786a4bf21d", 331 | "metadata": {}, 332 | "outputs": [], 333 | "source": [] 334 | }, 335 | { 336 | "cell_type": "code", 337 | "execution_count": null, 338 | "id": "812a3313-54cf-46d0-a4bf-183a8c65b662", 339 | "metadata": {}, 340 | "outputs": [], 341 | "source": [] 342 | }, 343 | { 344 | "cell_type": "markdown", 345 | "id": "fdd045af-baf6-43cb-b248-d479dd360470", 346 | "metadata": { 347 | "tags": [] 348 | }, 349 | "source": [ 350 | "## Visualization" 351 | ] 352 | }, 353 | { 354 | "cell_type": "code", 355 | "execution_count": null, 356 | "id": "c5eaf264-b958-4493-b8d7-df8897701254", 357 | "metadata": {}, 358 | "outputs": [], 359 | "source": [ 360 | "plt.scatter(x_train, y_train);" 361 | ] 362 | }, 363 | { 364 | "cell_type": "markdown", 365 | "id": "d618c9f1-c7dc-4c17-816d-e79817bc63b9", 366 | "metadata": { 367 | "tags": [] 368 | }, 369 | "source": [ 370 | "## Hypothesis function" 371 | ] 372 | }, 373 | { 374 | "cell_type": "markdown", 375 | "id": "aa055c38", 376 | "metadata": {}, 377 | "source": [ 378 | "In the context of univariate linear regression, where we work with a single feature, the equation representing the relationship between the independent variable and the dependent variable can be expressed as the **hypothesis function**:\n", 379 | "\n", 380 | "**ŷ = β₀ + β₁x**\n", 381 | "\n", 382 | "In this equation:\n", 383 | "\n", 384 | "- **ŷ** (y-hat): Represents the predicted value of the dependent variable, also known as the model output or response variable.\n", 385 | " \n", 386 | "- **x**: Denotes the independent variable, often referred to as the input or predictor variable.\n", 387 | "\n", 388 | "- **β₀** (beta-zero): This is the y-intercept of the regression line, sometimes called the **bias** term. \n", 389 | " It signifies the point where the line crosses the y-axis. A higher value for β₀ raises the entire line, while a lower value pushes it down.\n", 390 | "\n", 391 | "- **β₁** (beta-one): This represents the coefficient (or **weight**) associated with the independent variable x. \n", 392 | " It determines the slope of the regression line \n", 393 | " a larger β₁ results in a steeper line, whereas a smaller β₁ yields a flatter line.\n", 394 | "\n", 395 | "- Both **β₀** and **β₁** are considered model parameters, which are estimated during the training process to best fit the data." 396 | ] 397 | }, 398 | { 399 | "cell_type": "markdown", 400 | "id": "12f23631-06c4-4bb8-ab4b-80404fe656cb", 401 | "metadata": {}, 402 | "source": [ 403 | "![alt text](https://raw.githubusercontent.com/Ebimsv/Machine_Learning_Course/refs/heads/main/pics/hypothesis_function_lr.png)" 404 | ] 405 | }, 406 | { 407 | "cell_type": "markdown", 408 | "id": "4de17cf7", 409 | "metadata": {}, 410 | "source": [ 411 | "## Plotting regression line with random numbers" 412 | ] 413 | }, 414 | { 415 | "cell_type": "code", 416 | "execution_count": null, 417 | "id": "74295af9", 418 | "metadata": {}, 419 | "outputs": [], 420 | "source": [] 421 | }, 422 | { 423 | "cell_type": "markdown", 424 | "id": "727bcd89", 425 | "metadata": {}, 426 | "source": [ 427 | "## Create and Train the Linear Regression Model" 428 | ] 429 | }, 430 | { 431 | "cell_type": "code", 432 | "execution_count": null, 433 | "id": "5f62b03e-05e6-409a-a7a7-faedc458cff5", 434 | "metadata": {}, 435 | "outputs": [], 436 | "source": [] 437 | }, 438 | { 439 | "cell_type": "code", 440 | "execution_count": null, 441 | "id": "d1356813-1902-4990-b6a4-1054aa52b261", 442 | "metadata": {}, 443 | "outputs": [ 444 | { 445 | "data": { 446 | "text/plain": [ 447 | "(array([[0.04652973]]), array([7.11963843]))" 448 | ] 449 | }, 450 | "execution_count": 16, 451 | "metadata": {}, 452 | "output_type": "execute_result" 453 | } 454 | ], 455 | "source": [] 456 | }, 457 | { 458 | "cell_type": "markdown", 459 | "id": "e9fd95b4", 460 | "metadata": {}, 461 | "source": [ 462 | "## Make Predictions\n", 463 | "- After fitting the model, we can make predictions on the test data." 464 | ] 465 | }, 466 | { 467 | "cell_type": "code", 468 | "execution_count": null, 469 | "id": "04a99edc", 470 | "metadata": {}, 471 | "outputs": [], 472 | "source": [] 473 | }, 474 | { 475 | "cell_type": "markdown", 476 | "id": "45eaa22e", 477 | "metadata": {}, 478 | "source": [ 479 | "## Evaluate the Model\n", 480 | "Let's evaluate the performance of our model using Mean Squared Error `(MSE)` and `R-squared` score." 481 | ] 482 | }, 483 | { 484 | "cell_type": "code", 485 | "execution_count": null, 486 | "id": "61ed2dda", 487 | "metadata": {}, 488 | "outputs": [ 489 | { 490 | "name": "stdout", 491 | "output_type": "stream", 492 | "text": [ 493 | "Mean Squared Error: 10.20\n", 494 | "R-squared Score: 0.68\n" 495 | ] 496 | } 497 | ], 498 | "source": [] 499 | }, 500 | { 501 | "cell_type": "markdown", 502 | "id": "26670cc5", 503 | "metadata": {}, 504 | "source": [ 505 | "## Visualize the Results" 506 | ] 507 | }, 508 | { 509 | "cell_type": "code", 510 | "execution_count": null, 511 | "id": "b76f7f2f", 512 | "metadata": {}, 513 | "outputs": [], 514 | "source": [] 515 | }, 516 | { 517 | "cell_type": "markdown", 518 | "id": "bf533d40-3c5b-4664-be57-030f94804385", 519 | "metadata": {}, 520 | "source": [ 521 | "# Example 2: Automobile price prediction" 522 | ] 523 | }, 524 | { 525 | "cell_type": "markdown", 526 | "id": "b9652fb3", 527 | "metadata": {}, 528 | "source": [ 529 | "![Automobile Price](https://raw.githubusercontent.com/Ebimsv/Machine_Learning_Course/main/pics/car_price_prediction.png)" 530 | ] 531 | }, 532 | { 533 | "cell_type": "markdown", 534 | "id": "7b84fe69", 535 | "metadata": {}, 536 | "source": [ 537 | "**Dataset Description**: \n", 538 | "This dataset contains 26 columns, which likely include various features about automobiles, such as specifications, make, model, year, fuel type, and more. However, for our analysis, we've narrowed it down to two columns:\n", 539 | "- **Engine Size**: Measured in liters, this represents the volume of all the engine’s cylinders combined. It's a key determinant of the vehicle's power and efficiency.\n", 540 | "- **Price**: This is the target variable you want to predict, representing the market price of the automobile.\n", 541 | "\n", 542 | "**Importance of Engine Size in Price Prediction**:\n", 543 | "Engine size often correlates with performance characteristics such as horsepower and torque, which can significantly influence a car’s market price. Generally, vehicles with larger engines tend to be more powerful and are often priced higher, but there are exceptions depending on brand, model, and other features." 544 | ] 545 | }, 546 | { 547 | "cell_type": "markdown", 548 | "id": "8f70dcca", 549 | "metadata": {}, 550 | "source": [ 551 | "## Step 1: Import Libraries" 552 | ] 553 | }, 554 | { 555 | "cell_type": "code", 556 | "execution_count": null, 557 | "id": "3baf0404", 558 | "metadata": {}, 559 | "outputs": [], 560 | "source": [ 561 | "# Import necessary libraries \n", 562 | "import pandas as pd \n", 563 | "import numpy as np \n", 564 | "import matplotlib.pyplot as plt \n", 565 | "from sklearn.model_selection import train_test_split \n", 566 | "from sklearn.linear_model import LinearRegression \n", 567 | "from sklearn.preprocessing import StandardScaler \n", 568 | "from sklearn.metrics import mean_squared_error, r2_score " 569 | ] 570 | }, 571 | { 572 | "cell_type": "markdown", 573 | "id": "71328c92", 574 | "metadata": {}, 575 | "source": [ 576 | "## Step 2: Load the Data" 577 | ] 578 | }, 579 | { 580 | "cell_type": "code", 581 | "execution_count": null, 582 | "id": "4fe10042", 583 | "metadata": {}, 584 | "outputs": [ 585 | { 586 | "data": { 587 | "text/html": [ 588 | "
\n", 589 | "\n", 602 | "\n", 603 | " \n", 604 | " \n", 605 | " \n", 606 | " \n", 607 | " \n", 608 | " \n", 609 | " \n", 610 | " \n", 611 | " \n", 612 | " \n", 613 | " \n", 614 | " \n", 615 | " \n", 616 | " \n", 617 | " \n", 618 | " \n", 619 | " \n", 620 | " \n", 621 | " \n", 622 | " \n", 623 | " \n", 624 | " \n", 625 | " \n", 626 | " \n", 627 | " \n", 628 | " \n", 629 | " \n", 630 | " \n", 631 | " \n", 632 | " \n", 633 | " \n", 634 | " \n", 635 | " \n", 636 | " \n", 637 | " \n", 638 | " \n", 639 | " \n", 640 | " \n", 641 | " \n", 642 | " \n", 643 | " \n", 644 | " \n", 645 | " \n", 646 | " \n", 647 | " \n", 648 | " \n", 649 | " \n", 650 | " \n", 651 | " \n", 652 | " \n", 653 | " \n", 654 | " \n", 655 | " \n", 656 | " \n", 657 | " \n", 658 | " \n", 659 | " \n", 660 | " \n", 661 | " \n", 662 | " \n", 663 | " \n", 664 | " \n", 665 | " \n", 666 | " \n", 667 | " \n", 668 | " \n", 669 | " \n", 670 | " \n", 671 | " \n", 672 | " \n", 673 | " \n", 674 | " \n", 675 | " \n", 676 | " \n", 677 | " \n", 678 | " \n", 679 | " \n", 680 | " \n", 681 | " \n", 682 | " \n", 683 | " \n", 684 | " \n", 685 | " \n", 686 | " \n", 687 | " \n", 688 | " \n", 689 | " \n", 690 | " \n", 691 | " \n", 692 | " \n", 693 | " \n", 694 | " \n", 695 | " \n", 696 | " \n", 697 | " \n", 698 | " \n", 699 | " \n", 700 | " \n", 701 | " \n", 702 | " \n", 703 | " \n", 704 | " \n", 705 | " \n", 706 | " \n", 707 | " \n", 708 | " \n", 709 | " \n", 710 | " \n", 711 | " \n", 712 | " \n", 713 | " \n", 714 | " \n", 715 | " \n", 716 | " \n", 717 | " \n", 718 | " \n", 719 | " \n", 720 | " \n", 721 | " \n", 722 | " \n", 723 | " \n", 724 | " \n", 725 | " \n", 726 | " \n", 727 | " \n", 728 | " \n", 729 | " \n", 730 | " \n", 731 | " \n", 732 | " \n", 733 | " \n", 734 | " \n", 735 | " \n", 736 | " \n", 737 | " \n", 738 | " \n", 739 | " \n", 740 | " \n", 741 | " \n", 742 | " \n", 743 | " \n", 744 | " \n", 745 | " \n", 746 | " \n", 747 | " \n", 748 | " \n", 749 | " \n", 750 | " \n", 751 | "
symbolingnormalized-lossesmakefuel-typeaspirationnum-of-doorsbody-styledrive-wheelsengine-locationwheel-base...engine-sizefuel-systemborestrokecompression-ratiohorsepowerpeak-rpmcity-mpghighway-mpgprice
03?alfa-romerogasstdtwoconvertiblerwdfront88.6...130mpfi3.472.689.01115000212713495
13?alfa-romerogasstdtwoconvertiblerwdfront88.6...130mpfi3.472.689.01115000212716500
21?alfa-romerogasstdtwohatchbackrwdfront94.5...152mpfi2.683.479.01545000192616500
32164audigasstdfoursedanfwdfront99.8...109mpfi3.193.410.01025500243013950
42164audigasstdfoursedan4wdfront99.4...136mpfi3.193.48.01155500182217450
\n", 752 | "

5 rows × 26 columns

\n", 753 | "
" 754 | ], 755 | "text/plain": [ 756 | " symboling normalized-losses make fuel-type aspiration num-of-doors \\\n", 757 | "0 3 ? alfa-romero gas std two \n", 758 | "1 3 ? alfa-romero gas std two \n", 759 | "2 1 ? alfa-romero gas std two \n", 760 | "3 2 164 audi gas std four \n", 761 | "4 2 164 audi gas std four \n", 762 | "\n", 763 | " body-style drive-wheels engine-location wheel-base ... engine-size \\\n", 764 | "0 convertible rwd front 88.6 ... 130 \n", 765 | "1 convertible rwd front 88.6 ... 130 \n", 766 | "2 hatchback rwd front 94.5 ... 152 \n", 767 | "3 sedan fwd front 99.8 ... 109 \n", 768 | "4 sedan 4wd front 99.4 ... 136 \n", 769 | "\n", 770 | " fuel-system bore stroke compression-ratio horsepower peak-rpm city-mpg \\\n", 771 | "0 mpfi 3.47 2.68 9.0 111 5000 21 \n", 772 | "1 mpfi 3.47 2.68 9.0 111 5000 21 \n", 773 | "2 mpfi 2.68 3.47 9.0 154 5000 19 \n", 774 | "3 mpfi 3.19 3.4 10.0 102 5500 24 \n", 775 | "4 mpfi 3.19 3.4 8.0 115 5500 18 \n", 776 | "\n", 777 | " highway-mpg price \n", 778 | "0 27 13495 \n", 779 | "1 27 16500 \n", 780 | "2 26 16500 \n", 781 | "3 30 13950 \n", 782 | "4 22 17450 \n", 783 | "\n", 784 | "[5 rows x 26 columns]" 785 | ] 786 | }, 787 | "execution_count": 3, 788 | "metadata": {}, 789 | "output_type": "execute_result" 790 | } 791 | ], 792 | "source": [ 793 | "df = pd.read_csv('../Data/Regression/Automobile_data.csv') \n", 794 | "df.head() " 795 | ] 796 | }, 797 | { 798 | "cell_type": "markdown", 799 | "id": "7547b143", 800 | "metadata": {}, 801 | "source": [ 802 | "## Step 3: Select and Rename Relevant Columns\n", 803 | "- Filter to select the relevant columns: `engine-size` and `price`." 804 | ] 805 | }, 806 | { 807 | "cell_type": "code", 808 | "execution_count": null, 809 | "id": "3fd79301", 810 | "metadata": {}, 811 | "outputs": [], 812 | "source": [] 813 | }, 814 | { 815 | "cell_type": "markdown", 816 | "id": "71ddbf1a", 817 | "metadata": {}, 818 | "source": [ 819 | "## Step 4: Check Data Types" 820 | ] 821 | }, 822 | { 823 | "cell_type": "code", 824 | "execution_count": null, 825 | "id": "9bb19375", 826 | "metadata": {}, 827 | "outputs": [], 828 | "source": [] 829 | }, 830 | { 831 | "cell_type": "markdown", 832 | "id": "d1185f8f", 833 | "metadata": {}, 834 | "source": [ 835 | "## Step 5: Convert to Numeric" 836 | ] 837 | }, 838 | { 839 | "cell_type": "markdown", 840 | "id": "9416102b", 841 | "metadata": {}, 842 | "source": [ 843 | "- If the columns contain numeric data in string format (**price**), convert them to numeric, handling any non-numeric cases by **coercing** them to NaN." 844 | ] 845 | }, 846 | { 847 | "cell_type": "code", 848 | "execution_count": null, 849 | "id": "0920443f", 850 | "metadata": {}, 851 | "outputs": [], 852 | "source": [] 853 | }, 854 | { 855 | "cell_type": "markdown", 856 | "id": "e443db0b", 857 | "metadata": {}, 858 | "source": [ 859 | "## Step 6: Handle Missing Values" 860 | ] 861 | }, 862 | { 863 | "cell_type": "code", 864 | "execution_count": null, 865 | "id": "90c7cd7d", 866 | "metadata": {}, 867 | "outputs": [], 868 | "source": [] 869 | }, 870 | { 871 | "cell_type": "markdown", 872 | "id": "784efd41", 873 | "metadata": {}, 874 | "source": [ 875 | "### Option 1: Drop rows with NaN values " 876 | ] 877 | }, 878 | { 879 | "cell_type": "code", 880 | "execution_count": null, 881 | "id": "6347e0f0", 882 | "metadata": {}, 883 | "outputs": [], 884 | "source": [] 885 | }, 886 | { 887 | "cell_type": "markdown", 888 | "id": "7744ab1f", 889 | "metadata": {}, 890 | "source": [ 891 | "### Option 2: Fill missing values, if appropriate" 892 | ] 893 | }, 894 | { 895 | "cell_type": "code", 896 | "execution_count": null, 897 | "id": "99b7c4e4", 898 | "metadata": {}, 899 | "outputs": [], 900 | "source": [] 901 | }, 902 | { 903 | "cell_type": "markdown", 904 | "id": "db6e3188", 905 | "metadata": {}, 906 | "source": [ 907 | "## Step 7: Exploratory Data Analysis (EDA)" 908 | ] 909 | }, 910 | { 911 | "cell_type": "markdown", 912 | "id": "325afeb5", 913 | "metadata": {}, 914 | "source": [ 915 | "### 1. Plot the Data:\n", 916 | "- Visualize the relationship between `engine_size` and `price` using a scatter plot." 917 | ] 918 | }, 919 | { 920 | "cell_type": "code", 921 | "execution_count": null, 922 | "id": "d2b8c789", 923 | "metadata": {}, 924 | "outputs": [], 925 | "source": [] 926 | }, 927 | { 928 | "cell_type": "markdown", 929 | "id": "3394d7a4", 930 | "metadata": {}, 931 | "source": [ 932 | "### 2. Check Summary Statistics:\n", 933 | "- Get an overview of the data." 934 | ] 935 | }, 936 | { 937 | "cell_type": "code", 938 | "execution_count": null, 939 | "id": "4f431455", 940 | "metadata": {}, 941 | "outputs": [], 942 | "source": [] 943 | }, 944 | { 945 | "cell_type": "markdown", 946 | "id": "462a2e95", 947 | "metadata": {}, 948 | "source": [ 949 | "## Step 8: Prepare for Univariate Linear Regression" 950 | ] 951 | }, 952 | { 953 | "cell_type": "markdown", 954 | "id": "542f598a", 955 | "metadata": {}, 956 | "source": [ 957 | "### 1. Define Your Features and Target Variable:" 958 | ] 959 | }, 960 | { 961 | "cell_type": "code", 962 | "execution_count": null, 963 | "id": "3d172106", 964 | "metadata": {}, 965 | "outputs": [], 966 | "source": [] 967 | }, 968 | { 969 | "cell_type": "markdown", 970 | "id": "f9e7a97e", 971 | "metadata": {}, 972 | "source": [ 973 | "### 2. Split the Data into train and test" 974 | ] 975 | }, 976 | { 977 | "cell_type": "code", 978 | "execution_count": null, 979 | "id": "3f0e4dd6", 980 | "metadata": {}, 981 | "outputs": [], 982 | "source": [] 983 | }, 984 | { 985 | "cell_type": "code", 986 | "execution_count": null, 987 | "id": "b2010367", 988 | "metadata": {}, 989 | "outputs": [], 990 | "source": [] 991 | }, 992 | { 993 | "cell_type": "markdown", 994 | "id": "e42afc83", 995 | "metadata": {}, 996 | "source": [ 997 | "## Step 9: Standardize the Features and Target Variable " 998 | ] 999 | }, 1000 | { 1001 | "cell_type": "code", 1002 | "execution_count": null, 1003 | "id": "72ca203a", 1004 | "metadata": {}, 1005 | "outputs": [], 1006 | "source": [] 1007 | }, 1008 | { 1009 | "cell_type": "markdown", 1010 | "id": "bfd104fb", 1011 | "metadata": {}, 1012 | "source": [ 1013 | "## Step 10: Train the Linear Regression Model " 1014 | ] 1015 | }, 1016 | { 1017 | "cell_type": "code", 1018 | "execution_count": null, 1019 | "id": "57bb3cc0", 1020 | "metadata": {}, 1021 | "outputs": [], 1022 | "source": [] 1023 | }, 1024 | { 1025 | "cell_type": "markdown", 1026 | "id": "7e8e0435", 1027 | "metadata": {}, 1028 | "source": [ 1029 | "## Step 11: Make Predictions " 1030 | ] 1031 | }, 1032 | { 1033 | "cell_type": "code", 1034 | "execution_count": null, 1035 | "id": "2341089f", 1036 | "metadata": {}, 1037 | "outputs": [], 1038 | "source": [] 1039 | }, 1040 | { 1041 | "cell_type": "markdown", 1042 | "id": "cca0b7fe", 1043 | "metadata": {}, 1044 | "source": [ 1045 | "## Step 12: Inverse Transform the Predictions " 1046 | ] 1047 | }, 1048 | { 1049 | "cell_type": "code", 1050 | "execution_count": null, 1051 | "id": "1d5f22b5", 1052 | "metadata": {}, 1053 | "outputs": [], 1054 | "source": [] 1055 | }, 1056 | { 1057 | "cell_type": "markdown", 1058 | "id": "49ebf309", 1059 | "metadata": {}, 1060 | "source": [ 1061 | "## Step 13: Evaluate the Model, and Plot " 1062 | ] 1063 | }, 1064 | { 1065 | "cell_type": "code", 1066 | "execution_count": null, 1067 | "id": "eb5215c1", 1068 | "metadata": {}, 1069 | "outputs": [], 1070 | "source": [] 1071 | }, 1072 | { 1073 | "cell_type": "markdown", 1074 | "id": "f66bc249", 1075 | "metadata": {}, 1076 | "source": [ 1077 | "## Step 14: Plotting the Train and Test Data with the Linear Regression Line " 1078 | ] 1079 | }, 1080 | { 1081 | "cell_type": "code", 1082 | "execution_count": null, 1083 | "id": "76899fa9", 1084 | "metadata": {}, 1085 | "outputs": [], 1086 | "source": [] 1087 | }, 1088 | { 1089 | "cell_type": "markdown", 1090 | "id": "5173beca", 1091 | "metadata": {}, 1092 | "source": [ 1093 | "# Example 3: Univariate Linear Regression with LinearRegression and SGDRegressor in Scikit-Learn" 1094 | ] 1095 | }, 1096 | { 1097 | "cell_type": "markdown", 1098 | "id": "46931154", 1099 | "metadata": {}, 1100 | "source": [ 1101 | "## 1. Importing Libraries\n", 1102 | "Import the necessary libraries for data generation, modeling, evaluation, and visualization." 1103 | ] 1104 | }, 1105 | { 1106 | "cell_type": "code", 1107 | "execution_count": null, 1108 | "id": "f957111b", 1109 | "metadata": {}, 1110 | "outputs": [], 1111 | "source": [ 1112 | "import numpy as np\n", 1113 | "import matplotlib.pyplot as plt\n", 1114 | "from sklearn.datasets import make_regression\n", 1115 | "from sklearn.linear_model import LinearRegression, SGDRegressor\n", 1116 | "from sklearn.model_selection import train_test_split\n", 1117 | "from sklearn.metrics import mean_squared_error, r2_score" 1118 | ] 1119 | }, 1120 | { 1121 | "cell_type": "markdown", 1122 | "id": "3ee8098f", 1123 | "metadata": {}, 1124 | "source": [ 1125 | "## 2. Generating and Splitting the Dataset\n", 1126 | "Create a synthetic univariate dataset and split it into training and testing sets." 1127 | ] 1128 | }, 1129 | { 1130 | "cell_type": "code", 1131 | "execution_count": null, 1132 | "id": "8eafff6d", 1133 | "metadata": {}, 1134 | "outputs": [], 1135 | "source": [ 1136 | "# Generate a synthetic dataset (univariate)\n", 1137 | "X, y = make_regression(n_samples=200, n_features=1, noise=15, random_state=42)\n", 1138 | "\n", 1139 | "# Split data into training and testing sets\n", 1140 | "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)" 1141 | ] 1142 | }, 1143 | { 1144 | "cell_type": "markdown", 1145 | "id": "8330d353", 1146 | "metadata": {}, 1147 | "source": [ 1148 | "## 3. Defining and Training the Models\n", 1149 | "Train two regression models: Linear Regression and SGD Regressor." 1150 | ] 1151 | }, 1152 | { 1153 | "cell_type": "code", 1154 | "execution_count": null, 1155 | "id": "6bd74bdd", 1156 | "metadata": {}, 1157 | "outputs": [], 1158 | "source": [] 1159 | }, 1160 | { 1161 | "cell_type": "code", 1162 | "execution_count": null, 1163 | "id": "8f63397d", 1164 | "metadata": {}, 1165 | "outputs": [], 1166 | "source": [] 1167 | }, 1168 | { 1169 | "cell_type": "markdown", 1170 | "id": "647efb37", 1171 | "metadata": {}, 1172 | "source": [ 1173 | "## 4. Making Predictions\n", 1174 | "Generate predictions for the test data using both models." 1175 | ] 1176 | }, 1177 | { 1178 | "cell_type": "code", 1179 | "execution_count": null, 1180 | "id": "471f7e5f", 1181 | "metadata": {}, 1182 | "outputs": [], 1183 | "source": [] 1184 | }, 1185 | { 1186 | "cell_type": "markdown", 1187 | "id": "b127cc54", 1188 | "metadata": {}, 1189 | "source": [ 1190 | "## 5. Evaluating the Models\n", 1191 | "Compute and display the Mean Squared Error (MSE) and R-squared ($𝑅^2$) score for both models.\n" 1192 | ] 1193 | }, 1194 | { 1195 | "cell_type": "code", 1196 | "execution_count": null, 1197 | "id": "781f2f06", 1198 | "metadata": {}, 1199 | "outputs": [], 1200 | "source": [] 1201 | }, 1202 | { 1203 | "cell_type": "markdown", 1204 | "id": "23b4a18f", 1205 | "metadata": {}, 1206 | "source": [ 1207 | "## 6. Visualizing the Results\n", 1208 | "Create scatter plots of the test data with regression lines predicted by each model." 1209 | ] 1210 | }, 1211 | { 1212 | "cell_type": "code", 1213 | "execution_count": null, 1214 | "id": "44bb3bd1", 1215 | "metadata": {}, 1216 | "outputs": [], 1217 | "source": [] 1218 | }, 1219 | { 1220 | "cell_type": "code", 1221 | "execution_count": null, 1222 | "id": "d703cc38", 1223 | "metadata": {}, 1224 | "outputs": [], 1225 | "source": [] 1226 | } 1227 | ], 1228 | "metadata": { 1229 | "kernelspec": { 1230 | "display_name": "pytorch23", 1231 | "language": "python", 1232 | "name": "python3" 1233 | }, 1234 | "language_info": { 1235 | "codemirror_mode": { 1236 | "name": "ipython", 1237 | "version": 3 1238 | }, 1239 | "file_extension": ".py", 1240 | "mimetype": "text/x-python", 1241 | "name": "python", 1242 | "nbconvert_exporter": "python", 1243 | "pygments_lexer": "ipython3", 1244 | "version": "3.9.19" 1245 | } 1246 | }, 1247 | "nbformat": 4, 1248 | "nbformat_minor": 5 1249 | } 1250 | -------------------------------------------------------------------------------- /Before codes/03_Supervised_Learning/Templates/Template_07. Univariate_linear_regression_scratch.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# Univariate Linear Regression with Stochastic Gradient Descent from Scratch" 8 | ] 9 | }, 10 | { 11 | "cell_type": "markdown", 12 | "metadata": {}, 13 | "source": [ 14 | "This example demonstrates implementing univariate linear regression using Stochastic Gradient Descent (SGD) from scratch. We'll use Scikit-Learn's data generator, compute gradients manually, and iteratively optimize the model parameters. We'll explain each step in detail, focusing on the gradient computation." 15 | ] 16 | }, 17 | { 18 | "cell_type": "markdown", 19 | "metadata": {}, 20 | "source": [ 21 | "## 1. Introduction to Gradient Descent\n", 22 | "Gradient Descent is an optimization algorithm to minimize a loss function by iteratively updating the model parameters (weight 𝑤 and bias b).\n", 23 | "- **Loss Function**: Mean Squared Error (MSE):" 24 | ] 25 | }, 26 | { 27 | "cell_type": "markdown", 28 | "metadata": {}, 29 | "source": [ 30 | "![mse loss](https://raw.githubusercontent.com/Ebimsv/Machine_Learning_Course/refs/heads/main/pics/MSE.png)" 31 | ] 32 | }, 33 | { 34 | "cell_type": "markdown", 35 | "metadata": {}, 36 | "source": [ 37 | "- Here, 𝑥_𝑖 is the feature value, y_i is the true value, and w, b are the model parameters." 38 | ] 39 | }, 40 | { 41 | "cell_type": "markdown", 42 | "metadata": {}, 43 | "source": [ 44 | "- **Gradient Computation**: The gradients of the loss function w.r.t 𝑤 and b are derived as:" 45 | ] 46 | }, 47 | { 48 | "cell_type": "markdown", 49 | "metadata": {}, 50 | "source": [ 51 | "![Gradient computation](https://raw.githubusercontent.com/Ebimsv/Machine_Learning_Course/refs/heads/main/pics/Gradient_computation.png)" 52 | ] 53 | }, 54 | { 55 | "cell_type": "markdown", 56 | "metadata": {}, 57 | "source": [ 58 | "In Stochastic Gradient Descent, we compute these gradients for a single data point at a time:" 59 | ] 60 | }, 61 | { 62 | "cell_type": "markdown", 63 | "metadata": {}, 64 | "source": [ 65 | "![gradients_w_b](https://raw.githubusercontent.com/Ebimsv/Machine_Learning_Course/refs/heads/main/pics/gradients_w_b.png)" 66 | ] 67 | }, 68 | { 69 | "cell_type": "markdown", 70 | "metadata": {}, 71 | "source": [ 72 | "## 2. Importing Libraries" 73 | ] 74 | }, 75 | { 76 | "cell_type": "code", 77 | "execution_count": 19, 78 | "metadata": {}, 79 | "outputs": [], 80 | "source": [ 81 | "import numpy as np\n", 82 | "import matplotlib.pyplot as plt\n", 83 | "from sklearn.datasets import make_regression\n", 84 | "from sklearn.model_selection import train_test_split" 85 | ] 86 | }, 87 | { 88 | "cell_type": "markdown", 89 | "metadata": {}, 90 | "source": [ 91 | "## 3. Generate and Prepare Dataset\n", 92 | "We create a dataset with one feature (univariate), normalize it for faster convergence, and split it into training and testing sets." 93 | ] 94 | }, 95 | { 96 | "cell_type": "code", 97 | "execution_count": 20, 98 | "metadata": {}, 99 | "outputs": [], 100 | "source": [ 101 | "# Generate a synthetic dataset (univariate)\n", 102 | "X, y = make_regression(n_samples=200, n_features=1, noise=15, random_state=42)\n", 103 | "X = X.flatten() # Flatten X to 1D array\n", 104 | "\n", 105 | "# Normalize X and y\n", 106 | "X = (X - np.mean(X)) / np.std(X)\n", 107 | "y = (y - np.mean(y)) / np.std(y)\n", 108 | "\n", 109 | "# Split the dataset into training and testing sets\n", 110 | "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)" 111 | ] 112 | }, 113 | { 114 | "cell_type": "markdown", 115 | "metadata": {}, 116 | "source": [ 117 | "## 4. Initialize Parameters\n", 118 | "Randomly initialize the model parameters w (weight) and b (bias)." 119 | ] 120 | }, 121 | { 122 | "cell_type": "code", 123 | "execution_count": 21, 124 | "metadata": {}, 125 | "outputs": [], 126 | "source": [ 127 | "w = np.random.randn() # Random weight\n", 128 | "b = np.random.randn() # Random bias\n", 129 | "learning_rate = 0.01 # Step size for updates\n", 130 | "epochs = 1000 # Number of iterations" 131 | ] 132 | }, 133 | { 134 | "cell_type": "markdown", 135 | "metadata": {}, 136 | "source": [ 137 | "## 5. Define Stochastic Gradient Descent\n", 138 | "We implement the SGD algorithm by iterating through the dataset, computing the gradients for w and b, and updating the parameters." 139 | ] 140 | }, 141 | { 142 | "cell_type": "code", 143 | "execution_count": 1, 144 | "metadata": {}, 145 | "outputs": [], 146 | "source": [ 147 | "import numpy as np \n", 148 | "from sklearn.utils import shuffle as sklearn_shuffle \n", 149 | "\n", 150 | "def sgd(X, y, w, b, learning_rate, epochs): \n", 151 | " pass" 152 | ] 153 | }, 154 | { 155 | "cell_type": "markdown", 156 | "metadata": {}, 157 | "source": [ 158 | "## 6. Train the Model\n", 159 | "We train the model using the training dataset and store the final parameters and loss history." 160 | ] 161 | }, 162 | { 163 | "cell_type": "code", 164 | "execution_count": null, 165 | "metadata": {}, 166 | "outputs": [], 167 | "source": [] 168 | }, 169 | { 170 | "cell_type": "markdown", 171 | "metadata": {}, 172 | "source": [ 173 | "## 7. Evaluate the Model\n", 174 | "We evaluate the trained model on both the training and testing datasets by computing the Mean Squared Error (MSE)." 175 | ] 176 | }, 177 | { 178 | "cell_type": "code", 179 | "execution_count": null, 180 | "metadata": {}, 181 | "outputs": [], 182 | "source": [] 183 | }, 184 | { 185 | "cell_type": "markdown", 186 | "metadata": {}, 187 | "source": [ 188 | "## 8. Visualize the Results\n", 189 | "We visualize the loss history and compare predicted vs actual values." 190 | ] 191 | }, 192 | { 193 | "cell_type": "code", 194 | "execution_count": null, 195 | "metadata": {}, 196 | "outputs": [], 197 | "source": [] 198 | }, 199 | { 200 | "cell_type": "code", 201 | "execution_count": null, 202 | "metadata": {}, 203 | "outputs": [], 204 | "source": [] 205 | } 206 | ], 207 | "metadata": { 208 | "kernelspec": { 209 | "display_name": "pytorch23", 210 | "language": "python", 211 | "name": "python3" 212 | }, 213 | "language_info": { 214 | "codemirror_mode": { 215 | "name": "ipython", 216 | "version": 3 217 | }, 218 | "file_extension": ".py", 219 | "mimetype": "text/x-python", 220 | "name": "python", 221 | "nbconvert_exporter": "python", 222 | "pygments_lexer": "ipython3", 223 | "version": "3.9.19" 224 | } 225 | }, 226 | "nbformat": 4, 227 | "nbformat_minor": 2 228 | } 229 | -------------------------------------------------------------------------------- /Before codes/03_Supervised_Learning/Templates/Template_08. multiple_linear_regression.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# Analyzing Startup Success: A Multiple Linear Regression Approach Using the 50_Startups Dataset" 8 | ] 9 | }, 10 | { 11 | "cell_type": "markdown", 12 | "metadata": {}, 13 | "source": [ 14 | "![50 startups](https://raw.githubusercontent.com/Ebimsv/Machine_Learning_Course/refs/heads/main/pics/50_startups.png)" 15 | ] 16 | }, 17 | { 18 | "cell_type": "markdown", 19 | "metadata": {}, 20 | "source": [ 21 | "## Imports" 22 | ] 23 | }, 24 | { 25 | "cell_type": "code", 26 | "execution_count": 2, 27 | "metadata": {}, 28 | "outputs": [], 29 | "source": [ 30 | "import numpy as np \n", 31 | "import pandas as pd \n", 32 | "import matplotlib.pyplot as plt \n", 33 | "import seaborn as sns \n", 34 | "import missingno as msno \n", 35 | "from sklearn.model_selection import train_test_split \n", 36 | "from sklearn.linear_model import LinearRegression \n", 37 | "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score " 38 | ] 39 | }, 40 | { 41 | "cell_type": "markdown", 42 | "metadata": {}, 43 | "source": [ 44 | "## Load the dataset " 45 | ] 46 | }, 47 | { 48 | "cell_type": "code", 49 | "execution_count": 3, 50 | "metadata": {}, 51 | "outputs": [ 52 | { 53 | "data": { 54 | "text/html": [ 55 | "
\n", 56 | "\n", 69 | "\n", 70 | " \n", 71 | " \n", 72 | " \n", 73 | " \n", 74 | " \n", 75 | " \n", 76 | " \n", 77 | " \n", 78 | " \n", 79 | " \n", 80 | " \n", 81 | " \n", 82 | " \n", 83 | " \n", 84 | " \n", 85 | " \n", 86 | " \n", 87 | " \n", 88 | " \n", 89 | " \n", 90 | " \n", 91 | " \n", 92 | " \n", 93 | " \n", 94 | " \n", 95 | " \n", 96 | " \n", 97 | " \n", 98 | " \n", 99 | " \n", 100 | " \n", 101 | " \n", 102 | " \n", 103 | " \n", 104 | " \n", 105 | " \n", 106 | " \n", 107 | " \n", 108 | " \n", 109 | " \n", 110 | " \n", 111 | " \n", 112 | " \n", 113 | " \n", 114 | " \n", 115 | " \n", 116 | " \n", 117 | " \n", 118 | " \n", 119 | " \n", 120 | " \n", 121 | " \n", 122 | "
R&D SpendAdministrationMarketing SpendStateProfit
0165349.20136897.80471784.10New York192261.83
1166597.70151377.59443898.53California191792.06
2153441.51101145.55407934.54Florida191050.39
3144372.41118671.85383199.62New York182901.99
4142107.3491391.77366168.42Florida166187.94
\n", 123 | "
" 124 | ], 125 | "text/plain": [ 126 | " R&D Spend Administration Marketing Spend State Profit\n", 127 | "0 165349.20 136897.80 471784.10 New York 192261.83\n", 128 | "1 166597.70 151377.59 443898.53 California 191792.06\n", 129 | "2 153441.51 101145.55 407934.54 Florida 191050.39\n", 130 | "3 144372.41 118671.85 383199.62 New York 182901.99\n", 131 | "4 142107.34 91391.77 366168.42 Florida 166187.94" 132 | ] 133 | }, 134 | "execution_count": 3, 135 | "metadata": {}, 136 | "output_type": "execute_result" 137 | } 138 | ], 139 | "source": [ 140 | "df = pd.read_csv('../../Data/50_Startups.csv') \n", 141 | "df.head() " 142 | ] 143 | }, 144 | { 145 | "cell_type": "markdown", 146 | "metadata": {}, 147 | "source": [ 148 | "## Preprocessing" 149 | ] 150 | }, 151 | { 152 | "cell_type": "markdown", 153 | "metadata": {}, 154 | "source": [ 155 | "### 1. Check and Handle Missing Values\n", 156 | "Before proceeding with modeling, it's important to identify and handle any missing values in the dataset. \n", 157 | "This step includes printing the count of missing values per column and visualizing them using a matrix plot to understand their distribution." 158 | ] 159 | }, 160 | { 161 | "cell_type": "code", 162 | "execution_count": null, 163 | "metadata": {}, 164 | "outputs": [], 165 | "source": [] 166 | }, 167 | { 168 | "cell_type": "markdown", 169 | "metadata": {}, 170 | "source": [ 171 | "### 2. Imputation of Missing Values\n", 172 | "To ensure the dataset is complete, missing values in the 'Administration' and 'Marketing Spend' columns are filled with their respective medians. \n", 173 | "This approach preserves the data distribution while addressing gaps in the dataset." 174 | ] 175 | }, 176 | { 177 | "cell_type": "code", 178 | "execution_count": null, 179 | "metadata": {}, 180 | "outputs": [], 181 | "source": [] 182 | }, 183 | { 184 | "cell_type": "code", 185 | "execution_count": null, 186 | "metadata": {}, 187 | "outputs": [], 188 | "source": [] 189 | }, 190 | { 191 | "cell_type": "markdown", 192 | "metadata": {}, 193 | "source": [ 194 | "### 3. Convert Categorical Variables\n", 195 | "The 'State' categorical variable is converted into a category data type for better handling during analysis. This step is crucial for preparing the data for encoding." 196 | ] 197 | }, 198 | { 199 | "cell_type": "code", 200 | "execution_count": null, 201 | "metadata": {}, 202 | "outputs": [], 203 | "source": [] 204 | }, 205 | { 206 | "cell_type": "markdown", 207 | "metadata": {}, 208 | "source": [ 209 | "### 4. Encoding Categorical Variables\n", 210 | "To enable effective modeling, categorical variables are transformed into numerical format using one-hot encoding. \n", 211 | "This process creates binary columns for each category, allowing the regression model to utilize these features." 212 | ] 213 | }, 214 | { 215 | "cell_type": "code", 216 | "execution_count": null, 217 | "metadata": {}, 218 | "outputs": [], 219 | "source": [] 220 | }, 221 | { 222 | "cell_type": "markdown", 223 | "metadata": {}, 224 | "source": [ 225 | "### 5. Change Order of Columns\n", 226 | "Rearranging the columns into a logical order improves the readability of the dataset. \n", 227 | "This step ensures that similar attributes are grouped together, making it easier to navigate the data." 228 | ] 229 | }, 230 | { 231 | "cell_type": "code", 232 | "execution_count": null, 233 | "metadata": {}, 234 | "outputs": [], 235 | "source": [] 236 | }, 237 | { 238 | "cell_type": "markdown", 239 | "metadata": {}, 240 | "source": [ 241 | "### 6. Rename Columns for Better Readability\n", 242 | "To enhance the clarity of the dataset, specific columns are renamed. This makes it easier to interpret the data without confusion arising from spaces or lengthy names." 243 | ] 244 | }, 245 | { 246 | "cell_type": "code", 247 | "execution_count": null, 248 | "metadata": {}, 249 | "outputs": [], 250 | "source": [] 251 | }, 252 | { 253 | "cell_type": "markdown", 254 | "metadata": {}, 255 | "source": [ 256 | "### 7. Outlier Detection and Removal\n", 257 | "Outliers can significantly skew the results of data analysis and modeling. This step involves detecting and removing outliers for numerical columns in the dataset using the Interquartile Range (IQR) method. A boxplot is also generated for visualizing the distribution and identifying potential outliers within each relevant column." 258 | ] 259 | }, 260 | { 261 | "cell_type": "code", 262 | "execution_count": 4, 263 | "metadata": {}, 264 | "outputs": [], 265 | "source": [ 266 | "import pandas as pd \n", 267 | "import matplotlib.pyplot as plt \n", 268 | "import numpy as np \n", 269 | "\n", 270 | "# Function to detect and remove outliers for a single column \n", 271 | "def detect_and_remove_outliers(df, column_name, multiplier=1.5): \n", 272 | " \"\"\" \n", 273 | " Detect and remove outliers from a specified column in the DataFrame. \n", 274 | " \n", 275 | " Parameters: \n", 276 | " df (pd.DataFrame): The DataFrame from which to remove outliers. \n", 277 | " column_name (str): The column in which to detect outliers. \n", 278 | " multiplier (float): The multiplier for the IQR method to define outliers. \n", 279 | "\n", 280 | " Returns: \n", 281 | " pd.DataFrame: DataFrame without outliers. \n", 282 | " pd.DataFrame: Outliers detected in the specified column. \n", 283 | " \"\"\" \n", 284 | " # Calculate quantiles for outlier detection \n", 285 | " pass\n", 286 | "\n", 287 | " return df_no_outliers, outliers" 288 | ] 289 | }, 290 | { 291 | "cell_type": "code", 292 | "execution_count": null, 293 | "metadata": {}, 294 | "outputs": [], 295 | "source": [] 296 | }, 297 | { 298 | "cell_type": "code", 299 | "execution_count": null, 300 | "metadata": {}, 301 | "outputs": [], 302 | "source": [] 303 | }, 304 | { 305 | "cell_type": "code", 306 | "execution_count": null, 307 | "metadata": {}, 308 | "outputs": [], 309 | "source": [] 310 | }, 311 | { 312 | "cell_type": "markdown", 313 | "metadata": {}, 314 | "source": [ 315 | "## Feature Analysis and Selection Process for Predicting Profit" 316 | ] 317 | }, 318 | { 319 | "cell_type": "markdown", 320 | "metadata": {}, 321 | "source": [ 322 | "### 1. Correlation Analysis on Non-Binary Columns\n", 323 | "This section identifies non-binary columns and computes the correlation matrix." 324 | ] 325 | }, 326 | { 327 | "cell_type": "code", 328 | "execution_count": null, 329 | "metadata": {}, 330 | "outputs": [], 331 | "source": [] 332 | }, 333 | { 334 | "cell_type": "markdown", 335 | "metadata": {}, 336 | "source": [ 337 | "### 2. Visualize the Correlation Heatmap\n", 338 | "This section visualizes the correlation matrix using a heatmap for better interpretation." 339 | ] 340 | }, 341 | { 342 | "cell_type": "code", 343 | "execution_count": null, 344 | "metadata": {}, 345 | "outputs": [], 346 | "source": [] 347 | }, 348 | { 349 | "cell_type": "markdown", 350 | "metadata": {}, 351 | "source": [ 352 | "### 3. Interpret Correlations" 353 | ] 354 | }, 355 | { 356 | "cell_type": "markdown", 357 | "metadata": {}, 358 | "source": [ 359 | "**High Correlation**:\n", 360 | "- `R&D_Spend` has a very high correlation with Profit (0.979), suggesting it is a strong predictor.\n", 361 | "- `Marketing_Spend` also shows a notable positive correlation with Profit (0.718).\n", 362 | "\n", 363 | "**Low Correlation**: \n", 364 | "- `Administration` has a low correlation with Profit (0.121), indicating it may not be a significant predictor of profit." 365 | ] 366 | }, 367 | { 368 | "cell_type": "code", 369 | "execution_count": null, 370 | "metadata": {}, 371 | "outputs": [], 372 | "source": [] 373 | }, 374 | { 375 | "cell_type": "markdown", 376 | "metadata": {}, 377 | "source": [ 378 | "### 4. Feature Selection\n", 379 | "This section selects features for modeling based on the correlation analysis, typically by choosing those with significant correlation with the target variable." 380 | ] 381 | }, 382 | { 383 | "cell_type": "code", 384 | "execution_count": null, 385 | "metadata": {}, 386 | "outputs": [], 387 | "source": [] 388 | }, 389 | { 390 | "cell_type": "code", 391 | "execution_count": null, 392 | "metadata": {}, 393 | "outputs": [], 394 | "source": [] 395 | }, 396 | { 397 | "cell_type": "code", 398 | "execution_count": null, 399 | "metadata": {}, 400 | "outputs": [], 401 | "source": [] 402 | }, 403 | { 404 | "cell_type": "markdown", 405 | "metadata": {}, 406 | "source": [ 407 | "## Model Training " 408 | ] 409 | }, 410 | { 411 | "cell_type": "code", 412 | "execution_count": null, 413 | "metadata": {}, 414 | "outputs": [], 415 | "source": [] 416 | }, 417 | { 418 | "cell_type": "markdown", 419 | "metadata": {}, 420 | "source": [ 421 | "## 2. Model Parameters \n", 422 | "Display model intercept and coefficients " 423 | ] 424 | }, 425 | { 426 | "cell_type": "code", 427 | "execution_count": null, 428 | "metadata": {}, 429 | "outputs": [], 430 | "source": [] 431 | }, 432 | { 433 | "cell_type": "code", 434 | "execution_count": null, 435 | "metadata": {}, 436 | "outputs": [], 437 | "source": [] 438 | }, 439 | { 440 | "cell_type": "markdown", 441 | "metadata": {}, 442 | "source": [ 443 | "## Predictions and Model Evaluation " 444 | ] 445 | }, 446 | { 447 | "cell_type": "code", 448 | "execution_count": null, 449 | "metadata": {}, 450 | "outputs": [], 451 | "source": [] 452 | }, 453 | { 454 | "cell_type": "markdown", 455 | "metadata": {}, 456 | "source": [ 457 | "## Visualization (Optional) " 458 | ] 459 | }, 460 | { 461 | "cell_type": "code", 462 | "execution_count": null, 463 | "metadata": {}, 464 | "outputs": [ 465 | { 466 | "data": { 467 | "text/plain": [ 468 | "()" 469 | ] 470 | }, 471 | "execution_count": 5, 472 | "metadata": {}, 473 | "output_type": "execute_result" 474 | } 475 | ], 476 | "source": [] 477 | } 478 | ], 479 | "metadata": { 480 | "kernelspec": { 481 | "display_name": "pytorch23", 482 | "language": "python", 483 | "name": "python3" 484 | }, 485 | "language_info": { 486 | "codemirror_mode": { 487 | "name": "ipython", 488 | "version": 3 489 | }, 490 | "file_extension": ".py", 491 | "mimetype": "text/x-python", 492 | "name": "python", 493 | "nbconvert_exporter": "python", 494 | "pygments_lexer": "ipython3", 495 | "version": "3.9.19" 496 | } 497 | }, 498 | "nbformat": 4, 499 | "nbformat_minor": 2 500 | } 501 | -------------------------------------------------------------------------------- /Before codes/04_Unsupervised_Learning/14. Clustering.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Ebimsv/Machine_Learning_Course/a1a69e0baa6290f7860f76c2c27833b23a8f1302/Before codes/04_Unsupervised_Learning/14. Clustering.ipynb -------------------------------------------------------------------------------- /Before codes/04_Unsupervised_Learning/16. PCA.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Ebimsv/Machine_Learning_Course/a1a69e0baa6290f7860f76c2c27833b23a8f1302/Before codes/04_Unsupervised_Learning/16. PCA.ipynb -------------------------------------------------------------------------------- /Data/Data.csv: -------------------------------------------------------------------------------- 1 | Country,Age,Salary,Purchased 2 | France,44,72000,No 3 | Spain,27,48000,Yes 4 | Germany,30,54000,No 5 | Spain,38,61000,No 6 | Germany,40,,Yes 7 | France,35,58000,Yes 8 | Spain,,52000,No 9 | France,48,79000,Yes 10 | Germany,50,83000,No 11 | France,37,67000,Yes -------------------------------------------------------------------------------- /Data/Real_Estate_DataSet.csv: -------------------------------------------------------------------------------- 1 | CRIM,ZN,INDUS,CHAS,NOX,RM,AGE,DIS,RAD,TAX,PTRATIO,B,LSTAT,MEDV 2 | 0.00632,18,2.31,0,0.538,6.575,65.2,4.09,1,296,15.3,396.9,4.98,24 3 | 0.02731,0,7.07,0,0.469,6.421,78.9,4.9671,2,242,17.8,396.9,9.14,21.6 4 | 0.02729,0,7.07,0,0.469,7.185,61.1,4.9671,2,242,17.8,392.83,4.03,34.7 5 | 0.03237,0,2.18,0,0.458,6.998,45.8,6.0622,3,222,18.7,394.63,2.94,33.4 6 | 0.06905,0,2.18,0,0.458,7.147,54.2,6.0622,3,222,18.7,396.9,5.33,36.2 7 | 0.02985,0,2.18,0,0.458,6.43,58.7,6.0622,3,222,18.7,394.12,5.21,28.7 8 | 0.08829,12.5,7.87,0,0.524,6.012,66.6,5.5605,5,311,15.2,395.6,12.43,22.9 9 | 0.14455,12.5,7.87,0,0.524,6.172,96.1,5.9505,5,311,15.2,396.9,19.15,27.1 10 | 0.21124,12.5,7.87,0,0.524,5.631,100,6.0821,5,311,15.2,386.63,29.93,16.5 11 | 0.17004,12.5,7.87,0,0.524,6.004,85.9,6.5921,5,311,15.2,386.71,17.1,18.9 12 | 0.22489,12.5,7.87,0,0.524,,94.3,6.3467,5,311,15.2,392.52,20.45,15 13 | 0.11747,12.5,7.87,0,0.524,6.009,82.9,6.2267,5,311,15.2,396.9,13.27,18.9 14 | 0.09378,12.5,7.87,0,0.524,5.889,39,5.4509,5,311,15.2,390.5,15.71,21.7 15 | 0.62976,0,8.14,0,0.538,5.949,61.8,4.7075,4,307,21,396.9,8.26,20.4 16 | 0.63796,0,8.14,0,0.538,6.096,84.5,4.4619,4,307,21,380.02,10.26,18.2 17 | 0.62739,0,8.14,0,0.538,5.834,56.5,4.4986,4,307,21,395.62,8.47,19.9 18 | 1.05393,0,8.14,0,0.538,5.935,29.3,4.4986,4,307,21,386.85,6.58,23.1 19 | 0.7842,0,8.14,0,0.538,5.99,81.7,4.2579,4,307,21,386.75,14.67,17.5 20 | 0.80271,0,8.14,0,0.538,5.456,36.6,3.7965,4,307,21,288.99,11.69,20.2 21 | 0.7258,0,8.14,0,0.538,5.727,69.5,3.7965,4,307,21,390.95,11.28,18.2 22 | 1.25179,0,8.14,0,0.538,5.57,98.1,3.7979,4,307,21,376.57,21.02,13.6 23 | 0.85204,0,8.14,0,0.538,5.965,89.2,4.0123,4,307,21,392.53,13.83,19.6 24 | 1.23247,0,8.14,0,0.538,6.142,91.7,3.9769,4,307,21,396.9,18.72,15.2 25 | 0.98843,0,8.14,0,0.538,5.813,100,4.0952,4,307,21,394.54,19.88,14.5 26 | 0.75026,0,8.14,0,0.538,5.924,94.1,4.3996,4,307,21,394.33,16.3,15.6 27 | 0.84054,0,8.14,0,0.538,5.599,85.7,4.4546,4,307,21,303.42,16.51,13.9 28 | 0.67191,0,8.14,0,0.538,5.813,90.3,4.682,4,307,21,376.88,14.81,16.6 29 | 0.95577,0,8.14,0,0.538,6.047,88.8,4.4534,4,307,21,306.38,17.28,14.8 30 | 0.77299,0,8.14,0,0.538,6.495,94.4,4.4547,4,307,21,387.94,12.8,18.4 31 | 1.00245,0,8.14,0,0.538,6.674,87.3,4.239,4,307,21,380.23,11.98,21 32 | 1.13081,0,8.14,0,0.538,5.713,94.1,4.233,4,307,21,360.17,22.6,12.7 33 | 1.35472,0,8.14,0,0.538,6.072,100,4.175,4,307,21,376.73,13.04,14.5 34 | 1.38799,0,8.14,0,0.538,5.95,82,3.99,4,307,21,232.6,27.71,13.2 35 | 1.15172,0,8.14,0,0.538,5.701,95,3.7872,4,307,21,358.77,18.35,13.1 36 | 1.61282,0,8.14,0,0.538,6.096,96.9,3.7598,4,307,21,248.31,20.34,13.5 37 | 0.06417,0,5.96,0,0.499,,68.2,3.3603,5,279,19.2,396.9,9.68,18.9 38 | 0.09744,0,5.96,0,0.499,5.841,61.4,3.3779,5,279,19.2,377.56,11.41,20 39 | 0.08014,0,5.96,0,0.499,5.85,41.5,3.9342,5,279,19.2,396.9,8.77,21 40 | 0.17505,0,5.96,0,0.499,5.966,30.2,3.8473,5,279,19.2,393.43,10.13,24.7 41 | 0.02763,75,2.95,0,0.428,6.595,21.8,5.4011,3,252,18.3,395.63,4.32,30.8 42 | 0.03359,75,2.95,0,0.428,7.024,15.8,5.4011,3,252,18.3,395.62,1.98,34.9 43 | 0.12744,0,6.91,0,0.448,6.77,2.9,5.7209,3,233,17.9,385.41,4.84,26.6 44 | 0.1415,0,6.91,0,0.448,6.169,6.6,5.7209,3,233,17.9,383.37,5.81,25.3 45 | 0.15936,0,6.91,0,0.448,6.211,6.5,5.7209,3,233,17.9,394.46,7.44,24.7 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0.01432,100,1.32,0,0.411,6.816,40.5,8.3248,5,256,15.1,392.9,3.95,31.6 60 | 0.15445,25,5.13,0,0.453,6.145,29.2,7.8148,8,284,19.7,390.68,6.86,23.3 61 | 0.10328,25,5.13,0,0.453,5.927,47.2,6.932,8,284,19.7,396.9,9.22,19.6 62 | 0.14932,25,5.13,0,0.453,5.741,66.2,7.2254,8,284,19.7,395.11,13.15,18.7 63 | 0.17171,25,5.13,0,0.453,5.966,93.4,6.8185,8,284,19.7,378.08,14.44,16 64 | 0.11027,25,5.13,0,0.453,6.456,67.8,7.2255,8,284,19.7,396.9,6.73,22.2 65 | 0.1265,25,5.13,0,0.453,,43.4,7.9809,8,284,19.7,395.58,9.5,25 66 | 0.01951,17.5,1.38,0,0.4161,7.104,59.5,9.2229,3,216,18.6,393.24,8.05,33 67 | 0.03584,80,3.37,0,0.398,6.29,17.8,6.6115,4,337,16.1,396.9,4.67,23.5 68 | 0.04379,80,3.37,0,0.398,5.787,31.1,6.6115,4,337,16.1,396.9,10.24,19.4 69 | 0.05789,12.5,6.07,0,0.409,5.878,21.4,6.498,4,345,18.9,396.21,8.1,22 70 | 0.13554,12.5,6.07,0,0.409,5.594,36.8,6.498,4,345,18.9,396.9,13.09,17.4 71 | 0.12816,12.5,6.07,0,0.409,5.885,33,6.498,4,345,18.9,396.9,8.79,20.9 72 | 0.08826,0,10.81,0,0.413,6.417,6.6,5.2873,4,305,19.2,383.73,6.72,24.2 73 | 0.15876,0,10.81,0,0.413,5.961,17.5,5.2873,4,305,19.2,376.94,9.88,21.7 74 | 0.09164,0,10.81,0,0.413,6.065,7.8,5.2873,4,305,19.2,390.91,5.52,22.8 75 | 0.19539,0,10.81,0,0.413,6.245,6.2,5.2873,4,305,19.2,377.17,7.54,23.4 76 | 0.07896,0,12.83,0,0.437,6.273,6,4.2515,5,398,18.7,394.92,6.78,24.1 77 | 0.09512,0,12.83,0,0.437,6.286,45,4.5026,5,398,18.7,383.23,8.94,21.4 78 | 0.10153,0,12.83,0,0.437,6.279,74.5,4.0522,5,398,18.7,373.66,11.97,20 79 | 0.08707,0,12.83,0,0.437,6.14,45.8,4.0905,5,398,18.7,386.96,10.27,20.8 80 | 0.05646,0,12.83,0,0.437,6.232,53.7,5.0141,5,398,18.7,386.4,12.34,21.2 81 | 0.08387,0,12.83,0,0.437,5.874,36.6,4.5026,5,398,18.7,396.06,9.1,20.3 82 | 0.04113,25,4.86,0,0.426,6.727,33.5,5.4007,4,281,19,396.9,5.29,28 83 | 0.04462,25,4.86,0,0.426,6.619,70.4,5.4007,4,281,19,395.63,7.22,23.9 84 | 0.03659,25,4.86,0,0.426,6.302,32.2,5.4007,4,281,19,396.9,6.72,24.8 85 | 0.03551,25,4.86,0,0.426,6.167,46.7,5.4007,4,281,19,390.64,7.51,22.9 86 | 0.05059,0,4.49,0,0.449,6.389,48,4.7794,3,247,18.5,396.9,9.62,23.9 87 | 0.05735,0,4.49,0,0.449,6.63,56.1,4.4377,3,247,18.5,392.3,6.53,26.6 88 | 0.05188,0,4.49,0,0.449,6.015,45.1,4.4272,3,247,18.5,395.99,12.86,22.5 89 | 0.07151,0,4.49,0,0.449,6.121,56.8,3.7476,3,247,18.5,395.15,8.44,22.2 90 | 0.0566,0,3.41,0,0.489,7.007,86.3,3.4217,2,270,17.8,396.9,5.5,23.6 91 | 0.05302,0,3.41,0,0.489,7.079,63.1,3.4145,2,270,17.8,396.06,5.7,28.7 92 | 0.04684,0,3.41,0,0.489,6.417,66.1,3.0923,2,270,17.8,392.18,8.81,22.6 93 | 0.03932,0,3.41,0,0.489,6.405,73.9,3.0921,2,270,17.8,393.55,8.2,22 94 | 0.04203,28,15.04,0,0.464,6.442,53.6,3.6659,4,270,18.2,395.01,8.16,22.9 95 | 0.02875,28,15.04,0,0.464,6.211,28.9,3.6659,4,270,18.2,396.33,6.21,25 96 | 0.04294,28,15.04,0,0.464,6.249,77.3,3.615,4,270,18.2,396.9,10.59,20.6 97 | 0.12204,0,2.89,0,0.445,6.625,57.8,3.4952,2,276,18,357.98,6.65,28.4 98 | 0.11504,0,2.89,0,0.445,,69.6,3.4952,2,276,18,391.83,11.34,21.4 99 | 0.12083,0,2.89,0,0.445,8.069,76,3.4952,2,276,18,396.9,4.21,38.7 100 | 0.08187,0,2.89,0,0.445,7.82,36.9,3.4952,2,276,18,393.53,3.57,43.8 101 | 0.0686,0,2.89,0,0.445,7.416,62.5,3.4952,2,276,18,396.9,6.19,33.2 102 | 0.14866,0,8.56,0,0.52,6.727,79.9,2.7778,5,384,20.9,394.76,9.42,27.5 103 | 0.11432,0,8.56,0,0.52,6.781,71.3,2.8561,5,384,20.9,395.58,7.67,26.5 104 | 0.22876,0,8.56,0,0.52,6.405,85.4,2.7147,5,384,20.9,70.8,10.63,18.6 105 | 0.21161,0,8.56,0,0.52,6.137,87.4,2.7147,5,384,20.9,394.47,13.44,19.3 106 | 0.1396,0,8.56,0,0.52,6.167,90,2.421,5,384,20.9,392.69,12.33,20.1 107 | 0.13262,0,8.56,0,0.52,5.851,96.7,2.1069,5,384,20.9,394.05,16.47,19.5 108 | 0.1712,0,8.56,0,0.52,5.836,91.9,2.211,5,384,20.9,395.67,18.66,19.5 109 | 0.13117,0,8.56,0,0.52,6.127,85.2,2.1224,5,384,20.9,387.69,14.09,20.4 110 | 0.12802,0,8.56,0,0.52,6.474,97.1,2.4329,5,384,20.9,395.24,12.27,19.8 111 | 0.26363,0,8.56,0,0.52,6.229,91.2,2.5451,5,384,20.9,391.23,15.55,19.4 112 | 0.10793,0,8.56,0,0.52,6.195,54.4,2.7778,5,384,20.9,393.49,13,21.7 113 | 0.10084,0,10.01,0,0.547,6.715,81.6,2.6775,6,432,17.8,395.59,10.16,22.8 114 | 0.12329,0,10.01,0,0.547,5.913,92.9,2.3534,6,432,17.8,394.95,16.21,18.8 115 | 0.22212,0,10.01,0,0.547,6.092,95.4,2.548,6,432,17.8,396.9,17.09,18.7 116 | 0.14231,0,10.01,0,0.547,6.254,84.2,2.2565,6,432,17.8,388.74,10.45,18.5 117 | 0.17134,0,10.01,0,0.547,5.928,88.2,2.4631,6,432,17.8,344.91,15.76,18.3 118 | 0.13158,0,10.01,0,0.547,6.176,72.5,2.7301,6,432,17.8,393.3,12.04,21.2 119 | 0.15098,0,10.01,0,0.547,6.021,82.6,2.7474,6,432,17.8,394.51,10.3,19.2 120 | 0.13058,0,10.01,0,0.547,5.872,73.1,2.4775,6,432,17.8,338.63,15.37,20.4 121 | 0.14476,0,10.01,0,0.547,5.731,65.2,2.7592,6,432,17.8,391.5,13.61,19.3 122 | 0.06899,0,25.65,0,0.581,5.87,69.7,2.2577,2,188,19.1,389.15,14.37,22 123 | 0.07165,0,25.65,0,0.581,6.004,84.1,2.1974,2,188,19.1,377.67,14.27,20.3 124 | 0.09299,0,25.65,0,0.581,5.961,92.9,2.0869,2,188,19.1,378.09,17.93,20.5 125 | 0.15038,0,25.65,0,0.581,5.856,97,1.9444,2,188,19.1,370.31,25.41,17.3 126 | 0.09849,0,25.65,0,0.581,5.879,95.8,2.0063,2,188,19.1,379.38,17.58,18.8 127 | 0.16902,0,25.65,0,0.581,5.986,88.4,1.9929,2,188,19.1,385.02,14.81,21.4 128 | 0.38735,0,25.65,0,0.581,5.613,95.6,1.7572,2,188,19.1,359.29,27.26,15.7 129 | 0.25915,0,21.89,0,0.624,5.693,96,1.7883,4,437,21.2,392.11,17.19,16.2 130 | 0.32543,0,21.89,0,0.624,6.431,98.8,1.8125,4,437,21.2,396.9,15.39,18 131 | 0.88125,0,21.89,0,0.624,5.637,94.7,1.9799,4,437,21.2,396.9,18.34,14.3 132 | 0.34006,0,21.89,0,0.624,6.458,98.9,2.1185,4,437,21.2,395.04,12.6,19.2 133 | 1.19294,0,21.89,0,0.624,6.326,97.7,2.271,4,437,21.2,396.9,12.26,19.6 134 | 0.59005,0,21.89,0,0.624,6.372,97.9,2.3274,4,437,21.2,385.76,11.12,23 135 | 0.32982,0,21.89,0,0.624,5.822,95.4,2.4699,4,437,21.2,388.69,15.03,18.4 136 | 0.97617,0,21.89,0,0.624,5.757,98.4,2.346,4,437,21.2,262.76,17.31,15.6 137 | 0.55778,0,21.89,0,0.624,,98.2,2.1107,4,437,21.2,394.67,16.96,18.1 138 | 0.32264,0,21.89,0,0.624,5.942,93.5,1.9669,4,437,21.2,378.25,16.9,17.4 139 | 0.35233,0,21.89,0,0.624,6.454,98.4,1.8498,4,437,21.2,394.08,14.59,17.1 140 | 0.2498,0,21.89,0,0.624,5.857,98.2,1.6686,4,437,21.2,392.04,21.32,13.3 141 | 0.54452,0,21.89,0,0.624,6.151,97.9,1.6687,4,437,21.2,396.9,18.46,17.8 142 | 0.2909,0,21.89,0,0.624,6.174,93.6,1.6119,4,437,21.2,388.08,24.16,14 143 | 1.62864,0,21.89,0,0.624,5.019,100,1.4394,4,437,21.2,396.9,34.41,14.4 144 | 3.32105,0,19.58,1,0.871,5.403,100,1.3216,5,403,14.7,396.9,26.82,13.4 145 | 4.0974,0,19.58,0,0.871,5.468,100,1.4118,5,403,14.7,396.9,26.42,15.6 146 | 2.77974,0,19.58,0,0.871,4.903,97.8,1.3459,5,403,14.7,396.9,29.29,11.8 147 | 2.37934,0,19.58,0,0.871,6.13,100,1.4191,5,403,14.7,172.91,27.8,13.8 148 | 2.15505,0,19.58,0,0.871,5.628,100,1.5166,5,403,14.7,169.27,16.65,15.6 149 | 2.36862,0,19.58,0,0.871,4.926,95.7,1.4608,5,403,14.7,391.71,29.53,14.6 150 | 2.33099,0,19.58,0,0.871,5.186,93.8,1.5296,5,403,14.7,356.99,28.32,17.8 151 | 2.73397,0,19.58,0,0.871,5.597,94.9,1.5257,5,403,14.7,351.85,21.45,15.4 152 | 1.6566,0,19.58,0,0.871,6.122,97.3,1.618,5,403,14.7,372.8,14.1,21.5 153 | 1.49632,0,19.58,0,0.871,5.404,100,1.5916,5,403,14.7,341.6,13.28,19.6 154 | 1.12658,0,19.58,1,0.871,5.012,88,1.6102,5,403,14.7,343.28,12.12,15.3 155 | 2.14918,0,19.58,0,0.871,5.709,98.5,1.6232,5,403,14.7,261.95,15.79,19.4 156 | 1.41385,0,19.58,1,0.871,6.129,96,1.7494,5,403,14.7,321.02,15.12,17 157 | 3.53501,0,19.58,1,0.871,6.152,82.6,1.7455,5,403,14.7,88.01,15.02,15.6 158 | 2.44668,0,19.58,0,0.871,5.272,94,1.7364,5,403,14.7,88.63,16.14,13.1 159 | 1.22358,0,19.58,0,0.605,6.943,97.4,1.8773,5,403,14.7,363.43,4.59,41.3 160 | 1.34284,0,19.58,0,0.605,6.066,100,1.7573,5,403,14.7,353.89,6.43,24.3 161 | 1.42502,0,19.58,0,0.871,6.51,100,1.7659,5,403,14.7,364.31,7.39,23.3 162 | 1.27346,0,19.58,1,0.605,6.25,92.6,1.7984,5,403,14.7,338.92,5.5,27 163 | 1.46336,0,19.58,0,0.605,7.489,90.8,1.9709,5,403,14.7,374.43,1.73,50 164 | 1.83377,0,19.58,1,0.605,7.802,98.2,2.0407,5,403,14.7,389.61,1.92,50 165 | 1.51902,0,19.58,1,0.605,8.375,93.9,2.162,5,403,14.7,388.45,3.32,50 166 | 2.24236,0,19.58,0,0.605,5.854,91.8,2.422,5,403,14.7,395.11,11.64,22.7 167 | 2.924,0,19.58,0,0.605,6.101,93,2.2834,5,403,14.7,240.16,9.81,25 168 | 2.01019,0,19.58,0,0.605,7.929,96.2,2.0459,5,403,14.7,369.3,3.7,50 169 | 1.80028,0,19.58,0,0.605,5.877,79.2,2.4259,5,403,14.7,227.61,12.14,23.8 170 | 2.3004,0,19.58,0,0.605,6.319,96.1,2.1,5,403,14.7,297.09,11.1,23.8 171 | 2.44953,0,19.58,0,0.605,6.402,95.2,2.2625,5,403,14.7,330.04,11.32,22.3 172 | 1.20742,0,19.58,0,0.605,5.875,94.6,2.4259,5,403,14.7,292.29,14.43,17.4 173 | 2.3139,0,19.58,0,0.605,5.88,97.3,2.3887,5,403,14.7,348.13,12.03,19.1 174 | 0.13914,0,4.05,0,0.51,5.572,88.5,2.5961,5,296,16.6,396.9,14.69,23.1 175 | 0.09178,0,4.05,0,0.51,6.416,84.1,2.6463,5,296,16.6,395.5,9.04,23.6 176 | 0.08447,0,4.05,0,0.51,5.859,68.7,2.7019,5,296,16.6,393.23,9.64,22.6 177 | 0.06664,0,4.05,0,0.51,6.546,33.1,3.1323,5,296,16.6,390.96,5.33,29.4 178 | 0.07022,0,4.05,0,0.51,6.02,47.2,3.5549,5,296,16.6,393.23,10.11,23.2 179 | 0.05425,0,4.05,0,0.51,6.315,73.4,3.3175,5,296,16.6,395.6,6.29,24.6 180 | 0.06642,0,4.05,0,0.51,6.86,74.4,2.9153,5,296,16.6,391.27,6.92,29.9 181 | 0.0578,0,2.46,0,0.488,6.98,58.4,2.829,3,193,17.8,396.9,5.04,37.2 182 | 0.06588,0,2.46,0,0.488,7.765,83.3,2.741,3,193,17.8,395.56,7.56,39.8 183 | 0.06888,0,2.46,0,0.488,6.144,62.2,2.5979,3,193,17.8,396.9,9.45,36.2 184 | 0.09103,0,2.46,0,0.488,7.155,92.2,2.7006,3,193,17.8,394.12,4.82,37.9 185 | 0.10008,0,2.46,0,0.488,6.563,95.6,2.847,3,193,17.8,396.9,5.68,32.5 186 | 0.08308,0,2.46,0,0.488,5.604,89.8,2.9879,3,193,17.8,391,13.98,26.4 187 | 0.06047,0,2.46,0,0.488,6.153,68.8,3.2797,3,193,17.8,387.11,13.15,29.6 188 | 0.05602,0,2.46,0,0.488,7.831,53.6,3.1992,3,193,17.8,392.63,4.45,50 189 | 0.07875,45,3.44,0,0.437,6.782,41.1,3.7886,5,398,15.2,393.87,6.68,32 190 | 0.12579,45,3.44,0,0.437,6.556,29.1,4.5667,5,398,15.2,382.84,4.56,29.8 191 | 0.0837,45,3.44,0,0.437,7.185,38.9,4.5667,5,398,15.2,396.9,5.39,34.9 192 | 0.09068,45,3.44,0,0.437,6.951,21.5,6.4798,5,398,15.2,377.68,5.1,37 193 | 0.06911,45,3.44,0,0.437,6.739,30.8,6.4798,5,398,15.2,389.71,4.69,30.5 194 | 0.08664,45,3.44,0,0.437,7.178,26.3,6.4798,5,398,15.2,390.49,2.87,36.4 195 | 0.02187,60,2.93,0,0.401,6.8,9.9,6.2196,1,265,15.6,393.37,5.03,31.1 196 | 0.01439,60,2.93,0,0.401,6.604,18.8,6.2196,1,265,15.6,376.7,4.38,29.1 197 | 0.01381,80,0.46,0,0.422,7.875,32,5.6484,4,255,14.4,394.23,2.97,50 198 | 0.04011,80,1.52,0,0.404,7.287,34.1,7.309,2,329,12.6,396.9,4.08,33.3 199 | 0.04666,80,1.52,0,0.404,7.107,36.6,7.309,2,329,12.6,354.31,8.61,30.3 200 | 0.03768,80,1.52,0,0.404,7.274,38.3,7.309,2,329,12.6,392.2,6.62,34.6 201 | 0.0315,95,1.47,0,0.403,6.975,15.3,7.6534,3,402,17,396.9,4.56,34.9 202 | 0.01778,95,1.47,0,0.403,7.135,13.9,7.6534,3,402,17,384.3,4.45,32.9 203 | 0.03445,82.5,2.03,0,0.415,6.162,38.4,6.27,2,348,14.7,393.77,7.43,24.1 204 | 0.02177,82.5,2.03,0,0.415,7.61,15.7,6.27,2,348,14.7,395.38,3.11,42.3 205 | 0.0351,95,2.68,0,0.4161,7.853,33.2,5.118,4,224,14.7,392.78,3.81,48.5 206 | 0.02009,95,2.68,0,0.4161,8.034,31.9,5.118,4,224,14.7,390.55,2.88,50 207 | 0.13642,0,10.59,0,0.489,5.891,22.3,3.9454,4,277,18.6,396.9,10.87,22.6 208 | 0.22969,0,10.59,0,0.489,6.326,52.5,4.3549,4,277,18.6,394.87,10.97,24.4 209 | 0.25199,0,10.59,0,0.489,5.783,72.7,4.3549,4,277,18.6,389.43,18.06,22.5 210 | 0.13587,0,10.59,1,0.489,6.064,59.1,4.2392,4,277,18.6,381.32,14.66,24.4 211 | 0.43571,0,10.59,1,0.489,5.344,100,3.875,4,277,18.6,396.9,23.09,20 212 | 0.17446,0,10.59,1,0.489,5.96,92.1,3.8771,4,277,18.6,393.25,17.27,21.7 213 | 0.37578,0,10.59,1,0.489,5.404,88.6,3.665,4,277,18.6,395.24,23.98,19.3 214 | 0.21719,0,10.59,1,0.489,5.807,53.8,3.6526,4,277,18.6,390.94,16.03,22.4 215 | 0.14052,0,10.59,0,0.489,6.375,32.3,3.9454,4,277,18.6,385.81,9.38,28.1 216 | 0.28955,0,10.59,0,0.489,5.412,9.8,3.5875,4,277,18.6,348.93,29.55,23.7 217 | 0.19802,0,10.59,0,0.489,6.182,42.4,3.9454,4,277,18.6,393.63,9.47,25 218 | 0.0456,0,13.89,1,0.55,5.888,56,3.1121,5,276,16.4,392.8,13.51,23.3 219 | 0.07013,0,13.89,0,0.55,6.642,85.1,3.4211,5,276,16.4,392.78,9.69,28.7 220 | 0.11069,0,13.89,1,0.55,5.951,93.8,2.8893,5,276,16.4,396.9,17.92,21.5 221 | 0.11425,0,13.89,1,0.55,6.373,92.4,3.3633,5,276,16.4,393.74,10.5,23 222 | 0.35809,0,6.2,1,0.507,6.951,88.5,2.8617,8,307,17.4,391.7,9.71,26.7 223 | 0.40771,0,6.2,1,0.507,6.164,91.3,3.048,8,307,17.4,395.24,21.46,21.7 224 | 0.62356,0,6.2,1,0.507,6.879,77.7,3.2721,8,307,17.4,390.39,9.93,27.5 225 | 0.6147,0,6.2,0,0.507,6.618,80.8,3.2721,8,307,17.4,396.9,7.6,30.1 226 | 0.31533,0,6.2,0,0.504,8.266,78.3,2.8944,8,307,17.4,385.05,4.14,44.8 227 | 0.52693,0,6.2,0,0.504,8.725,83,2.8944,8,307,17.4,382,4.63,50 228 | 0.38214,0,6.2,0,0.504,8.04,86.5,3.2157,8,307,17.4,387.38,3.13,37.6 229 | 0.41238,0,6.2,0,0.504,7.163,79.9,3.2157,8,307,17.4,372.08,6.36,31.6 230 | 0.29819,0,6.2,0,0.504,7.686,17,3.3751,8,307,17.4,377.51,3.92,46.7 231 | 0.44178,0,6.2,0,0.504,6.552,21.4,3.3751,8,307,17.4,380.34,3.76,31.5 232 | 0.537,0,6.2,0,0.504,5.981,68.1,3.6715,8,307,17.4,378.35,11.65,24.3 233 | 0.46296,0,6.2,0,0.504,7.412,76.9,3.6715,8,307,17.4,376.14,5.25,31.7 234 | 0.57529,0,6.2,0,0.507,8.337,73.3,3.8384,8,307,17.4,385.91,2.47,41.7 235 | 0.33147,0,6.2,0,0.507,8.247,70.4,3.6519,8,307,17.4,378.95,3.95,48.3 236 | 0.44791,0,6.2,1,0.507,6.726,66.5,3.6519,8,307,17.4,360.2,8.05,29 237 | 0.33045,0,6.2,0,0.507,6.086,61.5,3.6519,8,307,17.4,376.75,10.88,24 238 | 0.52058,0,6.2,1,0.507,6.631,76.5,4.148,8,307,17.4,388.45,9.54,25.1 239 | 0.51183,0,6.2,0,0.507,7.358,71.6,4.148,8,307,17.4,390.07,4.73,31.5 240 | 0.08244,30,4.93,0,0.428,6.481,18.5,6.1899,6,300,16.6,379.41,6.36,23.7 241 | 0.09252,30,4.93,0,0.428,6.606,42.2,6.1899,6,300,16.6,383.78,7.37,23.3 242 | 0.11329,30,4.93,0,0.428,6.897,54.3,6.3361,6,300,16.6,391.25,11.38,22 243 | 0.10612,30,4.93,0,0.428,6.095,65.1,6.3361,6,300,16.6,394.62,12.4,20.1 244 | 0.1029,30,4.93,0,0.428,6.358,52.9,7.0355,6,300,16.6,372.75,11.22,22.2 245 | 0.12757,30,4.93,0,0.428,6.393,7.8,7.0355,6,300,16.6,374.71,5.19,23.7 246 | 0.20608,22,5.86,0,0.431,5.593,76.5,7.9549,7,330,19.1,372.49,12.5,17.6 247 | 0.19133,22,5.86,0,0.431,5.605,70.2,7.9549,7,330,19.1,389.13,18.46,18.5 248 | 0.33983,22,5.86,0,0.431,6.108,34.9,8.0555,7,330,19.1,390.18,9.16,24.3 249 | 0.19657,22,5.86,0,0.431,6.226,79.2,8.0555,7,330,19.1,376.14,10.15,20.5 250 | 0.16439,22,5.86,0,0.431,6.433,49.1,7.8265,7,330,19.1,374.71,9.52,24.5 251 | 0.19073,22,5.86,0,0.431,6.718,17.5,7.8265,7,330,19.1,393.74,6.56,26.2 252 | 0.1403,22,5.86,0,0.431,6.487,13,7.3967,7,330,19.1,396.28,5.9,24.4 253 | 0.21409,22,5.86,0,0.431,6.438,8.9,7.3967,7,330,19.1,377.07,3.59,24.8 254 | 0.08221,22,5.86,0,0.431,6.957,6.8,8.9067,7,330,19.1,386.09,3.53,29.6 255 | 0.36894,22,5.86,0,0.431,8.259,8.4,8.9067,7,330,19.1,396.9,3.54,42.8 256 | 0.04819,80,3.64,0,0.392,6.108,32,9.2203,1,315,16.4,392.89,6.57,21.9 257 | 0.03548,80,3.64,0,0.392,5.876,19.1,9.2203,1,315,16.4,395.18,9.25,20.9 258 | 0.01538,90,3.75,0,0.394,7.454,34.2,6.3361,3,244,15.9,386.34,3.11,44 259 | 0.61154,20,3.97,0,0.647,8.704,86.9,1.801,5,264,13,389.7,5.12,50 260 | 0.66351,20,3.97,0,0.647,7.333,100,1.8946,5,264,13,383.29,7.79,36 261 | 0.65665,20,3.97,0,0.647,6.842,100,2.0107,5,264,13,391.93,6.9,30.1 262 | 0.54011,20,3.97,0,0.647,7.203,81.8,2.1121,5,264,13,392.8,9.59,33.8 263 | 0.53412,20,3.97,0,0.647,7.52,89.4,2.1398,5,264,13,388.37,7.26,43.1 264 | 0.52014,20,3.97,0,0.647,8.398,91.5,2.2885,5,264,13,386.86,5.91,48.8 265 | 0.82526,20,3.97,0,0.647,7.327,94.5,2.0788,5,264,13,393.42,11.25,31 266 | 0.55007,20,3.97,0,0.647,7.206,91.6,1.9301,5,264,13,387.89,8.1,36.5 267 | 0.76162,20,3.97,0,0.647,5.56,62.8,1.9865,5,264,13,392.4,10.45,22.8 268 | 0.7857,20,3.97,0,0.647,7.014,84.6,2.1329,5,264,13,384.07,14.79,30.7 269 | 0.57834,20,3.97,0,0.575,8.297,67,2.4216,5,264,13,384.54,7.44,50 270 | 0.5405,20,3.97,0,0.575,7.47,52.6,2.872,5,264,13,390.3,3.16,43.5 271 | 0.09065,20,6.96,1,0.464,5.92,61.5,3.9175,3,223,18.6,391.34,13.65,20.7 272 | 0.29916,20,6.96,0,0.464,5.856,42.1,4.429,3,223,18.6,388.65,13,21.1 273 | 0.16211,20,6.96,0,0.464,6.24,16.3,4.429,3,223,18.6,396.9,6.59,25.2 274 | 0.1146,20,6.96,0,0.464,6.538,58.7,3.9175,3,223,18.6,394.96,7.73,24.4 275 | 0.22188,20,6.96,1,0.464,7.691,51.8,4.3665,3,223,18.6,390.77,6.58,35.2 276 | 0.05644,40,6.41,1,0.447,6.758,32.9,4.0776,4,254,17.6,396.9,3.53,32.4 277 | 0.09604,40,6.41,0,0.447,6.854,42.8,4.2673,4,254,17.6,396.9,2.98,32 278 | 0.10469,40,6.41,1,0.447,7.267,49,4.7872,4,254,17.6,389.25,6.05,33.2 279 | 0.06127,40,6.41,1,0.447,6.826,27.6,4.8628,4,254,17.6,393.45,4.16,33.1 280 | 0.07978,40,6.41,0,0.447,6.482,32.1,4.1403,4,254,17.6,396.9,7.19,29.1 281 | 0.21038,20,3.33,0,0.4429,6.812,32.2,4.1007,5,216,14.9,396.9,4.85,35.1 282 | 0.03578,20,3.33,0,0.4429,7.82,64.5,4.6947,5,216,14.9,387.31,3.76,45.4 283 | 0.03705,20,3.33,0,0.4429,6.968,37.2,5.2447,5,216,14.9,392.23,4.59,35.4 284 | 0.06129,20,3.33,1,0.4429,7.645,49.7,5.2119,5,216,14.9,377.07,3.01,46 285 | 0.01501,90,1.21,1,0.401,7.923,24.8,5.885,1,198,13.6,395.52,3.16,50 286 | 0.00906,90,2.97,0,0.4,7.088,20.8,7.3073,1,285,15.3,394.72,7.85,32.2 287 | 0.01096,55,2.25,0,0.389,6.453,31.9,7.3073,1,300,15.3,394.72,8.23,22 288 | 0.01965,80,1.76,0,0.385,6.23,31.5,9.0892,1,241,18.2,341.6,12.93,20.1 289 | 0.03871,52.5,5.32,0,0.405,6.209,31.3,7.3172,6,293,16.6,396.9,7.14,23.2 290 | 0.0459,52.5,5.32,0,0.405,6.315,45.6,7.3172,6,293,16.6,396.9,7.6,22.3 291 | 0.04297,52.5,5.32,0,0.405,6.565,22.9,7.3172,6,293,16.6,371.72,9.51,24.8 292 | 0.03502,80,4.95,0,0.411,6.861,27.9,5.1167,4,245,19.2,396.9,3.33,28.5 293 | 0.07886,80,4.95,0,0.411,7.148,27.7,5.1167,4,245,19.2,396.9,3.56,37.3 294 | 0.03615,80,4.95,0,0.411,6.63,23.4,5.1167,4,245,19.2,396.9,4.7,27.9 295 | 0.08265,0,13.92,0,0.437,6.127,18.4,5.5027,4,289,16,396.9,8.58,23.9 296 | 0.08199,0,13.92,0,0.437,6.009,42.3,5.5027,4,289,16,396.9,10.4,21.7 297 | 0.12932,0,13.92,0,0.437,6.678,31.1,5.9604,4,289,16,396.9,6.27,28.6 298 | 0.05372,0,13.92,0,0.437,6.549,51,5.9604,4,289,16,392.85,7.39,27.1 299 | 0.14103,0,13.92,0,0.437,5.79,58,6.32,4,289,16,396.9,15.84,20.3 300 | 0.06466,70,2.24,0,0.4,6.345,20.1,7.8278,5,358,14.8,368.24,4.97,22.5 301 | 0.05561,70,2.24,0,0.4,7.041,10,7.8278,5,358,14.8,371.58,4.74,29 302 | 0.04417,70,2.24,0,0.4,6.871,47.4,7.8278,5,358,14.8,390.86,6.07,24.8 303 | 0.03537,34,6.09,0,0.433,6.59,40.4,5.4917,7,329,16.1,395.75,9.5,22 304 | 0.09266,34,6.09,0,0.433,6.495,18.4,5.4917,7,329,16.1,383.61,8.67,26.4 305 | 0.1,34,6.09,0,0.433,6.982,17.7,5.4917,7,329,16.1,390.43,4.86,33.1 306 | 0.05515,33,2.18,0,0.472,7.236,41.1,4.022,7,222,18.4,393.68,6.93,36.1 307 | 0.05479,33,2.18,0,0.472,6.616,58.1,3.37,7,222,18.4,393.36,8.93,28.4 308 | 0.07503,33,2.18,0,0.472,7.42,71.9,3.0992,7,222,18.4,396.9,6.47,33.4 309 | 0.04932,33,2.18,0,0.472,6.849,70.3,3.1827,7,222,18.4,396.9,7.53,28.2 310 | 0.49298,0,9.9,0,0.544,6.635,82.5,3.3175,4,304,18.4,396.9,4.54,22.8 311 | 0.3494,0,9.9,0,0.544,5.972,76.7,3.1025,4,304,18.4,396.24,9.97,20.3 312 | 2.63548,0,9.9,0,0.544,4.973,37.8,2.5194,4,304,18.4,350.45,12.64,16.1 313 | 0.79041,0,9.9,0,0.544,6.122,52.8,2.6403,4,304,18.4,396.9,5.98,22.1 314 | 0.26169,0,9.9,0,0.544,6.023,90.4,2.834,4,304,18.4,396.3,11.72,19.4 315 | 0.26938,0,9.9,0,0.544,6.266,82.8,3.2628,4,304,18.4,393.39,7.9,21.6 316 | 0.3692,0,9.9,0,0.544,6.567,87.3,3.6023,4,304,18.4,395.69,9.28,23.8 317 | 0.25356,0,9.9,0,0.544,5.705,77.7,3.945,4,304,18.4,396.42,11.5,16.2 318 | 0.31827,0,9.9,0,0.544,5.914,83.2,3.9986,4,304,18.4,390.7,18.33,17.8 319 | 0.24522,0,9.9,0,0.544,5.782,71.7,4.0317,4,304,18.4,396.9,15.94,19.8 320 | 0.40202,0,9.9,0,0.544,6.382,67.2,3.5325,4,304,18.4,395.21,10.36,23.1 321 | 0.47547,0,9.9,0,0.544,6.113,58.8,4.0019,4,304,18.4,396.23,12.73,21 322 | 0.1676,0,7.38,0,0.493,6.426,52.3,4.5404,5,287,19.6,396.9,7.2,23.8 323 | 0.18159,0,7.38,0,0.493,6.376,54.3,4.5404,5,287,19.6,396.9,6.87,23.1 324 | 0.35114,0,7.38,0,0.493,6.041,49.9,4.7211,5,287,19.6,396.9,7.7,20.4 325 | 0.28392,0,7.38,0,0.493,5.708,74.3,4.7211,5,287,19.6,391.13,11.74,18.5 326 | 0.34109,0,7.38,0,0.493,6.415,40.1,4.7211,5,287,19.6,396.9,6.12,25 327 | 0.19186,0,7.38,0,0.493,6.431,14.7,5.4159,5,287,19.6,393.68,5.08,24.6 328 | 0.30347,0,7.38,0,0.493,6.312,28.9,5.4159,5,287,19.6,396.9,6.15,23 329 | 0.24103,0,7.38,0,0.493,6.083,43.7,5.4159,5,287,19.6,396.9,12.79,22.2 330 | 0.06617,0,3.24,0,0.46,5.868,25.8,5.2146,4,430,16.9,382.44,9.97,19.3 331 | 0.06724,0,3.24,0,0.46,6.333,17.2,5.2146,4,430,16.9,375.21,7.34,22.6 332 | 0.04544,0,3.24,0,0.46,6.144,32.2,5.8736,4,430,16.9,368.57,9.09,19.8 333 | 0.05023,35,6.06,0,0.4379,5.706,28.4,6.6407,1,304,16.9,394.02,12.43,17.1 334 | 0.03466,35,6.06,0,0.4379,6.031,23.3,6.6407,1,304,16.9,362.25,7.83,19.4 335 | 0.05083,0,5.19,0,0.515,6.316,38.1,6.4584,5,224,20.2,389.71,5.68,22.2 336 | 0.03738,0,5.19,0,0.515,6.31,38.5,6.4584,5,224,20.2,389.4,6.75,20.7 337 | 0.03961,0,5.19,0,0.515,6.037,34.5,5.9853,5,224,20.2,396.9,8.01,21.1 338 | 0.03427,0,5.19,0,0.515,5.869,46.3,5.2311,5,224,20.2,396.9,9.8,19.5 339 | 0.03041,0,5.19,0,0.515,5.895,59.6,5.615,5,224,20.2,394.81,10.56,18.5 340 | 0.03306,0,5.19,0,0.515,6.059,37.3,4.8122,5,224,20.2,396.14,8.51,20.6 341 | 0.05497,0,5.19,0,0.515,5.985,45.4,4.8122,5,224,20.2,396.9,9.74,19 342 | 0.06151,0,5.19,0,0.515,5.968,58.5,4.8122,5,224,20.2,396.9,9.29,18.7 343 | 0.01301,35,1.52,0,0.442,7.241,49.3,7.0379,1,284,15.5,394.74,5.49,32.7 344 | 0.02498,0,1.89,0,0.518,6.54,59.7,6.2669,1,422,15.9,389.96,8.65,16.5 345 | 0.02543,55,3.78,0,0.484,6.696,56.4,5.7321,5,370,17.6,396.9,7.18,23.9 346 | 0.03049,55,3.78,0,0.484,6.874,28.1,6.4654,5,370,17.6,387.97,4.61,31.2 347 | 0.03113,0,4.39,0,0.442,6.014,48.5,8.0136,3,352,18.8,385.64,10.53,17.5 348 | 0.06162,0,4.39,0,0.442,5.898,52.3,8.0136,3,352,18.8,364.61,12.67,17.2 349 | 0.0187,85,4.15,0,0.429,6.516,27.7,8.5353,4,351,17.9,392.43,6.36,23.1 350 | 0.01501,80,2.01,0,0.435,6.635,29.7,8.344,4,280,17,390.94,5.99,24.5 351 | 0.02899,40,1.25,0,0.429,6.939,34.5,8.7921,1,335,19.7,389.85,5.89,26.6 352 | 0.06211,40,1.25,0,0.429,6.49,44.4,8.7921,1,335,19.7,396.9,5.98,22.9 353 | 0.0795,60,1.69,0,0.411,6.579,35.9,10.7103,4,411,18.3,370.78,5.49,24.1 354 | 0.07244,60,1.69,0,0.411,5.884,18.5,10.7103,4,411,18.3,392.33,7.79,18.6 355 | 0.01709,90,2.02,0,0.41,6.728,36.1,12.1265,5,187,17,384.46,4.5,30.1 356 | 0.04301,80,1.91,0,0.413,5.663,21.9,10.5857,4,334,22,382.8,8.05,18.2 357 | 0.10659,80,1.91,0,0.413,5.936,19.5,10.5857,4,334,22,376.04,5.57,20.6 358 | 8.98296,0,18.1,1,0.77,6.212,97.4,2.1222,24,666,20.2,377.73,17.6,17.8 359 | 3.8497,0,18.1,1,0.77,6.395,91,2.5052,24,666,20.2,391.34,13.27,21.7 360 | 5.20177,0,18.1,1,0.77,6.127,83.4,2.7227,24,666,20.2,395.43,11.48,22.7 361 | 4.26131,0,18.1,0,0.77,6.112,81.3,2.5091,24,666,20.2,390.74,12.67,22.6 362 | 4.54192,0,18.1,0,0.77,6.398,88,2.5182,24,666,20.2,374.56,7.79,25 363 | 3.83684,0,18.1,0,0.77,6.251,91.1,2.2955,24,666,20.2,350.65,14.19,19.9 364 | 3.67822,0,18.1,0,0.77,5.362,96.2,2.1036,24,666,20.2,380.79,10.19,20.8 365 | 4.22239,0,18.1,1,0.77,5.803,89,1.9047,24,666,20.2,353.04,14.64,16.8 366 | 3.47428,0,18.1,1,0.718,8.78,82.9,1.9047,24,666,20.2,354.55,5.29,21.9 367 | 4.55587,0,18.1,0,0.718,3.561,87.9,1.6132,24,666,20.2,354.7,7.12,27.5 368 | 3.69695,0,18.1,0,0.718,4.963,91.4,1.7523,24,666,20.2,316.03,14,21.9 369 | 13.5222,0,18.1,0,0.631,3.863,100,1.5106,24,666,20.2,131.42,13.33,23.1 370 | 4.89822,0,18.1,0,0.631,4.97,100,1.3325,24,666,20.2,375.52,3.26,50 371 | 5.66998,0,18.1,1,0.631,6.683,96.8,1.3567,24,666,20.2,375.33,3.73,50 372 | 6.53876,0,18.1,1,0.631,7.016,97.5,1.2024,24,666,20.2,392.05,2.96,50 373 | 9.2323,0,18.1,0,0.631,6.216,100,1.1691,24,666,20.2,366.15,9.53,50 374 | 8.26725,0,18.1,1,0.668,5.875,89.6,1.1296,24,666,20.2,347.88,8.88,50 375 | 11.1081,0,18.1,0,0.668,4.906,100,1.1742,24,666,20.2,396.9,34.77,13.8 376 | 18.4982,0,18.1,0,0.668,4.138,100,1.137,24,666,20.2,396.9,37.97,13.8 377 | 19.6091,0,18.1,0,0.671,7.313,97.9,1.3163,24,666,20.2,396.9,13.44,15 378 | 15.288,0,18.1,0,0.671,6.649,93.3,1.3449,24,666,20.2,363.02,23.24,13.9 379 | 9.82349,0,18.1,0,0.671,6.794,98.8,1.358,24,666,20.2,396.9,21.24,13.3 380 | 23.6482,0,18.1,0,0.671,6.38,96.2,1.3861,24,666,20.2,396.9,23.69,13.1 381 | 17.8667,0,18.1,0,0.671,6.223,100,1.3861,24,666,20.2,393.74,21.78,10.2 382 | 88.9762,0,18.1,0,0.671,6.968,91.9,1.4165,24,666,20.2,396.9,17.21,10.4 383 | 15.8744,0,18.1,0,0.671,6.545,99.1,1.5192,24,666,20.2,396.9,21.08,10.9 384 | 9.18702,0,18.1,0,0.7,5.536,100,1.5804,24,666,20.2,396.9,23.6,11.3 385 | 7.99248,0,18.1,0,0.7,5.52,100,1.5331,24,666,20.2,396.9,24.56,12.3 386 | 20.0849,0,18.1,0,0.7,4.368,91.2,1.4395,24,666,20.2,285.83,30.63,8.8 387 | 16.8118,0,18.1,0,0.7,5.277,98.1,1.4261,24,666,20.2,396.9,30.81,7.2 388 | 24.3938,0,18.1,0,0.7,4.652,100,1.4672,24,666,20.2,396.9,28.28,10.5 389 | 22.5971,0,18.1,0,0.7,5,89.5,1.5184,24,666,20.2,396.9,31.99,7.4 390 | 14.3337,0,18.1,0,0.7,4.88,100,1.5895,24,666,20.2,372.92,30.62,10.2 391 | 8.15174,0,18.1,0,0.7,5.39,98.9,1.7281,24,666,20.2,396.9,20.85,11.5 392 | 6.96215,0,18.1,0,0.7,5.713,97,1.9265,24,666,20.2,394.43,17.11,15.1 393 | 5.29305,0,18.1,0,0.7,6.051,82.5,2.1678,24,666,20.2,378.38,18.76,23.2 394 | 11.5779,0,18.1,0,0.7,5.036,97,1.77,24,666,20.2,396.9,25.68,9.7 395 | 8.64476,0,18.1,0,0.693,6.193,92.6,1.7912,24,666,20.2,396.9,15.17,13.8 396 | 13.3598,0,18.1,0,0.693,5.887,94.7,1.7821,24,666,20.2,396.9,16.35,12.7 397 | 8.71675,0,18.1,0,0.693,6.471,98.8,1.7257,24,666,20.2,391.98,17.12,13.1 398 | 5.87205,0,18.1,0,0.693,6.405,96,1.6768,24,666,20.2,396.9,19.37,12.5 399 | 7.67202,0,18.1,0,0.693,5.747,98.9,1.6334,24,666,20.2,393.1,19.92,8.5 400 | 38.3518,0,18.1,0,0.693,5.453,100,1.4896,24,666,20.2,396.9,30.59,5 401 | 9.91655,0,18.1,0,0.693,5.852,77.8,1.5004,24,666,20.2,338.16,29.97,6.3 402 | 25.0461,0,18.1,0,0.693,5.987,100,1.5888,24,666,20.2,396.9,26.77,5.6 403 | 14.2362,0,18.1,0,0.693,6.343,100,1.5741,24,666,20.2,396.9,20.32,7.2 404 | 9.59571,0,18.1,0,0.693,6.404,100,1.639,24,666,20.2,376.11,20.31,12.1 405 | 24.8017,0,18.1,0,0.693,5.349,96,1.7028,24,666,20.2,396.9,19.77,8.3 406 | 41.5292,0,18.1,0,0.693,5.531,85.4,1.6074,24,666,20.2,329.46,27.38,8.5 407 | 67.9208,0,18.1,0,0.693,5.683,100,1.4254,24,666,20.2,384.97,22.98,5 408 | 20.7162,0,18.1,0,0.659,4.138,100,1.1781,24,666,20.2,370.22,23.34,11.9 409 | 11.9511,0,18.1,0,0.659,5.608,100,1.2852,24,666,20.2,332.09,12.13,27.9 410 | 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-------------------------------------------------------------------------------- 1 | R&D Spend,Administration,Marketing Spend,State,Profit 2 | 165349.2,136897.8,471784.1,New York,192261.83 3 | 166597.7,151377.59,443898.53,California,191792.06 4 | 153441.51,101145.55,407934.54,Florida,191050.39 5 | 144372.41,118671.85,383199.62,New York,182901.99 6 | 142107.34,91391.77,366168.42,Florida,166187.94 7 | 131876.9,,362861.36,New York,156991.12 8 | 134615.46,147198.87,127716.82,California,156122.51 9 | 130298.13,145530.06,323876.68,Florida,155752.6 10 | 120542.52,148718.95,311613.29,New York,152211.77 11 | 123334.88,108679.17,304981.62,California,149759.96 12 | 101913.08,110594.11,,Florida,146121.95 13 | 220000.6,91790.61,249744.55,California,144259.4 14 | 93863.75,127320.38,249839.44,Florida,141585.52 15 | 91992.39,135495.07,252664.93,California,134307.35 16 | 119943.24,156547.42,256512.92,Florida,132602.65 17 | 114523.61,122616.84,261776.23,New York,129917.04 18 | 78013.11,121597.55,264346.06,California,126992.93 19 | 94657.16,,282574.31,New York,125370.37 20 | 91749.16,114175.79,294919.57,Florida,124266.9 21 | 86419.7,153514.11,0,New York,122776.86 22 | 76253.86,113867.3,298664.47,California,118474.03 23 | 78389.47,153773.43,299737.29,New York,111313.02 24 | 73994.56,122782.75,303319.26,Florida,110352.25 25 | 67532.53,105751.03,304768.73,Florida,108733.99 26 | 77044.01,99281.34,140574.81,New York,108552.04 27 | 64664.71,139553.16,137962.62,California,107404.34 28 | 75328.87,144135.98,134050.07,Florida,105733.54 29 | 72107.6,127864.55,353183.81,New York,105008.31 30 | 66051.52,182645.56,118148.2,Florida,103282.38 31 | 65605.48,153032.06,107138.38,New York,101004.64 32 | 61994.48,115641.28,91131.24,Florida,99937.59 33 | 61136.38,152701.92,88218.23,New York,97483.56 34 | 63408.86,129219.61,46085.25,California,97427.84 35 | 55493.95,103057.49,214634.81,Florida,96778.92 36 | 46426.07,157693.92,210797.67,California,96712.8 37 | 46014.02,85047.44,205517.64,New York,96479.51 38 | 28663.76,127056.21,201126.82,Florida,90708.19 39 | 44069.95,51283.14,197029.42,California,89949.14 40 | 20229.59,65947.93,185265.1,New York,81229.06 41 | 38558.51,82982.09,174999.3,California,81005.76 42 | 28754.33,118546.05,172795.67,California,78239.91 43 | 27892.92,84710.77,164470.71,Florida,77798.83 44 | 23640.93,96189.63,148001.11,California,71498.49 45 | 15505.73,127382.3,35534.17,New York,69758.98 46 | 22177.74,154806.14,28334.72,California,65200.33 47 | 1000.23,124153.04,1903.93,New York,64926.08 48 | 1315.46,115816.21,297114.46,Florida,49490.75 49 | 50,135426.92,0,California,42559.73 50 | 542.05,51743.15,0,New York,35673.41 51 | 0,116983.8,45173.06,California,14681.4 52 | -------------------------------------------------------------------------------- /Data/Regression/Advertising.csv: -------------------------------------------------------------------------------- 1 | ,TV,Radio,Newspaper,Sales 2 | 1,230.1,37.8,69.2,22.1 3 | 2,44.5,39.3,45.1,10.4 4 | 3,17.2,45.9,69.3,9.3 5 | 4,151.5,41.3,58.5,18.5 6 | 5,180.8,10.8,58.4,12.9 7 | 6,8.7,48.9,75,7.2 8 | 7,57.5,32.8,23.5,11.8 9 | 8,120.2,19.6,11.6,13.2 10 | 9,8.6,2.1,1,4.8 11 | 10,199.8,2.6,21.2,10.6 12 | 11,66.1,5.8,24.2,8.6 13 | 12,214.7,24,4,17.4 14 | 13,23.8,35.1,65.9,9.2 15 | 14,97.5,7.6,7.2,9.7 16 | 15,204.1,32.9,46,19 17 | 16,195.4,47.7,52.9,22.4 18 | 17,67.8,36.6,114,12.5 19 | 18,281.4,39.6,55.8,24.4 20 | 19,69.2,20.5,18.3,11.3 21 | 20,147.3,23.9,19.1,14.6 22 | 21,218.4,27.7,53.4,18 23 | 22,237.4,5.1,23.5,12.5 24 | 23,13.2,15.9,49.6,5.6 25 | 24,228.3,16.9,26.2,15.5 26 | 25,62.3,12.6,18.3,9.7 27 | 26,262.9,3.5,19.5,12 28 | 27,142.9,29.3,12.6,15 29 | 28,240.1,16.7,22.9,15.9 30 | 29,248.8,27.1,22.9,18.9 31 | 30,70.6,16,40.8,10.5 32 | 31,292.9,28.3,43.2,21.4 33 | 32,112.9,17.4,38.6,11.9 34 | 33,97.2,1.5,30,9.6 35 | 34,265.6,20,0.3,17.4 36 | 35,95.7,1.4,7.4,9.5 37 | 36,290.7,4.1,8.5,12.8 38 | 37,266.9,43.8,5,25.4 39 | 38,74.7,49.4,45.7,14.7 40 | 39,43.1,26.7,35.1,10.1 41 | 40,228,37.7,32,21.5 42 | 41,202.5,22.3,31.6,16.6 43 | 42,177,33.4,38.7,17.1 44 | 43,293.6,27.7,1.8,20.7 45 | 44,206.9,8.4,26.4,12.9 46 | 45,25.1,25.7,43.3,8.5 47 | 46,175.1,22.5,31.5,14.9 48 | 47,89.7,9.9,35.7,10.6 49 | 48,239.9,41.5,18.5,23.2 50 | 49,227.2,15.8,49.9,14.8 51 | 50,66.9,11.7,36.8,9.7 52 | 51,199.8,3.1,34.6,11.4 53 | 52,100.4,9.6,3.6,10.7 54 | 53,216.4,41.7,39.6,22.6 55 | 54,182.6,46.2,58.7,21.2 56 | 55,262.7,28.8,15.9,20.2 57 | 56,198.9,49.4,60,23.7 58 | 57,7.3,28.1,41.4,5.5 59 | 58,136.2,19.2,16.6,13.2 60 | 59,210.8,49.6,37.7,23.8 61 | 60,210.7,29.5,9.3,18.4 62 | 61,53.5,2,21.4,8.1 63 | 62,261.3,42.7,54.7,24.2 64 | 63,239.3,15.5,27.3,15.7 65 | 64,102.7,29.6,8.4,14 66 | 65,131.1,42.8,28.9,18 67 | 66,69,9.3,0.9,9.3 68 | 67,31.5,24.6,2.2,9.5 69 | 68,139.3,14.5,10.2,13.4 70 | 69,237.4,27.5,11,18.9 71 | 70,216.8,43.9,27.2,22.3 72 | 71,199.1,30.6,38.7,18.3 73 | 72,109.8,14.3,31.7,12.4 74 | 73,26.8,33,19.3,8.8 75 | 74,129.4,5.7,31.3,11 76 | 75,213.4,24.6,13.1,17 77 | 76,16.9,43.7,89.4,8.7 78 | 77,27.5,1.6,20.7,6.9 79 | 78,120.5,28.5,14.2,14.2 80 | 79,5.4,29.9,9.4,5.3 81 | 80,116,7.7,23.1,11 82 | 81,76.4,26.7,22.3,11.8 83 | 82,239.8,4.1,36.9,12.3 84 | 83,75.3,20.3,32.5,11.3 85 | 84,68.4,44.5,35.6,13.6 86 | 85,213.5,43,33.8,21.7 87 | 86,193.2,18.4,65.7,15.2 88 | 87,76.3,27.5,16,12 89 | 88,110.7,40.6,63.2,16 90 | 89,88.3,25.5,73.4,12.9 91 | 90,109.8,47.8,51.4,16.7 92 | 91,134.3,4.9,9.3,11.2 93 | 92,28.6,1.5,33,7.3 94 | 93,217.7,33.5,59,19.4 95 | 94,250.9,36.5,72.3,22.2 96 | 95,107.4,14,10.9,11.5 97 | 96,163.3,31.6,52.9,16.9 98 | 97,197.6,3.5,5.9,11.7 99 | 98,184.9,21,22,15.5 100 | 99,289.7,42.3,51.2,25.4 101 | 100,135.2,41.7,45.9,17.2 102 | 101,222.4,4.3,49.8,11.7 103 | 102,296.4,36.3,100.9,23.8 104 | 103,280.2,10.1,21.4,14.8 105 | 104,187.9,17.2,17.9,14.7 106 | 105,238.2,34.3,5.3,20.7 107 | 106,137.9,46.4,59,19.2 108 | 107,25,11,29.7,7.2 109 | 108,90.4,0.3,23.2,8.7 110 | 109,13.1,0.4,25.6,5.3 111 | 110,255.4,26.9,5.5,19.8 112 | 111,225.8,8.2,56.5,13.4 113 | 112,241.7,38,23.2,21.8 114 | 113,175.7,15.4,2.4,14.1 115 | 114,209.6,20.6,10.7,15.9 116 | 115,78.2,46.8,34.5,14.6 117 | 116,75.1,35,52.7,12.6 118 | 117,139.2,14.3,25.6,12.2 119 | 118,76.4,0.8,14.8,9.4 120 | 119,125.7,36.9,79.2,15.9 121 | 120,19.4,16,22.3,6.6 122 | 121,141.3,26.8,46.2,15.5 123 | 122,18.8,21.7,50.4,7 124 | 123,224,2.4,15.6,11.6 125 | 124,123.1,34.6,12.4,15.2 126 | 125,229.5,32.3,74.2,19.7 127 | 126,87.2,11.8,25.9,10.6 128 | 127,7.8,38.9,50.6,6.6 129 | 128,80.2,0,9.2,8.8 130 | 129,220.3,49,3.2,24.7 131 | 130,59.6,12,43.1,9.7 132 | 131,0.7,39.6,8.7,1.6 133 | 132,265.2,2.9,43,12.7 134 | 133,8.4,27.2,2.1,5.7 135 | 134,219.8,33.5,45.1,19.6 136 | 135,36.9,38.6,65.6,10.8 137 | 136,48.3,47,8.5,11.6 138 | 137,25.6,39,9.3,9.5 139 | 138,273.7,28.9,59.7,20.8 140 | 139,43,25.9,20.5,9.6 141 | 140,184.9,43.9,1.7,20.7 142 | 141,73.4,17,12.9,10.9 143 | 142,193.7,35.4,75.6,19.2 144 | 143,220.5,33.2,37.9,20.1 145 | 144,104.6,5.7,34.4,10.4 146 | 145,96.2,14.8,38.9,11.4 147 | 146,140.3,1.9,9,10.3 148 | 147,240.1,7.3,8.7,13.2 149 | 148,243.2,49,44.3,25.4 150 | 149,38,40.3,11.9,10.9 151 | 150,44.7,25.8,20.6,10.1 152 | 151,280.7,13.9,37,16.1 153 | 152,121,8.4,48.7,11.6 154 | 153,197.6,23.3,14.2,16.6 155 | 154,171.3,39.7,37.7,19 156 | 155,187.8,21.1,9.5,15.6 157 | 156,4.1,11.6,5.7,3.2 158 | 157,93.9,43.5,50.5,15.3 159 | 158,149.8,1.3,24.3,10.1 160 | 159,11.7,36.9,45.2,7.3 161 | 160,131.7,18.4,34.6,12.9 162 | 161,172.5,18.1,30.7,14.4 163 | 162,85.7,35.8,49.3,13.3 164 | 163,188.4,18.1,25.6,14.9 165 | 164,163.5,36.8,7.4,18 166 | 165,117.2,14.7,5.4,11.9 167 | 166,234.5,3.4,84.8,11.9 168 | 167,17.9,37.6,21.6,8 169 | 168,206.8,5.2,19.4,12.2 170 | 169,215.4,23.6,57.6,17.1 171 | 170,284.3,10.6,6.4,15 172 | 171,50,11.6,18.4,8.4 173 | 172,164.5,20.9,47.4,14.5 174 | 173,19.6,20.1,17,7.6 175 | 174,168.4,7.1,12.8,11.7 176 | 175,222.4,3.4,13.1,11.5 177 | 176,276.9,48.9,41.8,27 178 | 177,248.4,30.2,20.3,20.2 179 | 178,170.2,7.8,35.2,11.7 180 | 179,276.7,2.3,23.7,11.8 181 | 180,165.6,10,17.6,12.6 182 | 181,156.6,2.6,8.3,10.5 183 | 182,218.5,5.4,27.4,12.2 184 | 183,56.2,5.7,29.7,8.7 185 | 184,287.6,43,71.8,26.2 186 | 185,253.8,21.3,30,17.6 187 | 186,205,45.1,19.6,22.6 188 | 187,139.5,2.1,26.6,10.3 189 | 188,191.1,28.7,18.2,17.3 190 | 189,286,13.9,3.7,15.9 191 | 190,18.7,12.1,23.4,6.7 192 | 191,39.5,41.1,5.8,10.8 193 | 192,75.5,10.8,6,9.9 194 | 193,17.2,4.1,31.6,5.9 195 | 194,166.8,42,3.6,19.6 196 | 195,149.7,35.6,6,17.3 197 | 196,38.2,3.7,13.8,7.6 198 | 197,94.2,4.9,8.1,9.7 199 | 198,177,9.3,6.4,12.8 200 | 199,283.6,42,66.2,25.5 201 | 200,232.1,8.6,8.7,13.4 202 | -------------------------------------------------------------------------------- /Data/Regression/Automobile_data.csv: -------------------------------------------------------------------------------- 1 | symboling,normalized-losses,make,fuel-type,aspiration,num-of-doors,body-style,drive-wheels,engine-location,wheel-base,length,width,height,curb-weight,engine-type,num-of-cylinders,engine-size,fuel-system,bore,stroke,compression-ratio,horsepower,peak-rpm,city-mpg,highway-mpg,price 2 | 3,?,alfa-romero,gas,std,two,convertible,rwd,front,88.6,168.8,64.1,48.8,2548,dohc,four,130,mpfi,3.47,2.68,9,111,5000,21,27,13495 3 | 3,?,alfa-romero,gas,std,two,convertible,rwd,front,88.6,168.8,64.1,48.8,2548,dohc,four,130,mpfi,3.47,2.68,9,111,5000,21,27,16500 4 | 1,?,alfa-romero,gas,std,two,hatchback,rwd,front,94.5,171.2,65.5,52.4,2823,ohcv,six,152,mpfi,2.68,3.47,9,154,5000,19,26,16500 5 | 2,164,audi,gas,std,four,sedan,fwd,front,99.8,176.6,66.2,54.3,2337,ohc,four,109,mpfi,3.19,3.4,10,102,5500,24,30,13950 6 | 2,164,audi,gas,std,four,sedan,4wd,front,99.4,176.6,66.4,54.3,2824,ohc,five,136,mpfi,3.19,3.4,8,115,5500,18,22,17450 7 | 2,?,audi,gas,std,two,sedan,fwd,front,99.8,177.3,66.3,53.1,2507,ohc,five,136,mpfi,3.19,3.4,8.5,110,5500,19,25,15250 8 | 1,158,audi,gas,std,four,sedan,fwd,front,105.8,192.7,71.4,55.7,2844,ohc,five,136,mpfi,3.19,3.4,8.5,110,5500,19,25,17710 9 | 1,?,audi,gas,std,four,wagon,fwd,front,105.8,192.7,71.4,55.7,2954,ohc,five,136,mpfi,3.19,3.4,8.5,110,5500,19,25,18920 10 | 1,158,audi,gas,turbo,four,sedan,fwd,front,105.8,192.7,71.4,55.9,3086,ohc,five,131,mpfi,3.13,3.4,8.3,140,5500,17,20,23875 11 | 0,?,audi,gas,turbo,two,hatchback,4wd,front,99.5,178.2,67.9,52,3053,ohc,five,131,mpfi,3.13,3.4,7,160,5500,16,22,? 12 | 2,192,bmw,gas,std,two,sedan,rwd,front,101.2,176.8,64.8,54.3,2395,ohc,four,108,mpfi,3.5,2.8,8.8,101,5800,23,29,16430 13 | 0,192,bmw,gas,std,four,sedan,rwd,front,101.2,176.8,64.8,54.3,2395,ohc,four,108,mpfi,3.5,2.8,8.8,101,5800,23,29,16925 14 | 0,188,bmw,gas,std,two,sedan,rwd,front,101.2,176.8,64.8,54.3,2710,ohc,six,164,mpfi,3.31,3.19,9,121,4250,21,28,20970 15 | 0,188,bmw,gas,std,four,sedan,rwd,front,101.2,176.8,64.8,54.3,2765,ohc,six,164,mpfi,3.31,3.19,9,121,4250,21,28,21105 16 | 1,?,bmw,gas,std,four,sedan,rwd,front,103.5,189,66.9,55.7,3055,ohc,six,164,mpfi,3.31,3.19,9,121,4250,20,25,24565 17 | 0,?,bmw,gas,std,four,sedan,rwd,front,103.5,189,66.9,55.7,3230,ohc,six,209,mpfi,3.62,3.39,8,182,5400,16,22,30760 18 | 0,?,bmw,gas,std,two,sedan,rwd,front,103.5,193.8,67.9,53.7,3380,ohc,six,209,mpfi,3.62,3.39,8,182,5400,16,22,41315 19 | 0,?,bmw,gas,std,four,sedan,rwd,front,110,197,70.9,56.3,3505,ohc,six,209,mpfi,3.62,3.39,8,182,5400,15,20,36880 20 | 2,121,chevrolet,gas,std,two,hatchback,fwd,front,88.4,141.1,60.3,53.2,1488,l,three,61,2bbl,2.91,3.03,9.5,48,5100,47,53,5151 21 | 1,98,chevrolet,gas,std,two,hatchback,fwd,front,94.5,155.9,63.6,52,1874,ohc,four,90,2bbl,3.03,3.11,9.6,70,5400,38,43,6295 22 | 0,81,chevrolet,gas,std,four,sedan,fwd,front,94.5,158.8,63.6,52,1909,ohc,four,90,2bbl,3.03,3.11,9.6,70,5400,38,43,6575 23 | 1,118,dodge,gas,std,two,hatchback,fwd,front,93.7,157.3,63.8,50.8,1876,ohc,four,90,2bbl,2.97,3.23,9.41,68,5500,37,41,5572 24 | 1,118,dodge,gas,std,two,hatchback,fwd,front,93.7,157.3,63.8,50.8,1876,ohc,four,90,2bbl,2.97,3.23,9.4,68,5500,31,38,6377 25 | 1,118,dodge,gas,turbo,two,hatchback,fwd,front,93.7,157.3,63.8,50.8,2128,ohc,four,98,mpfi,3.03,3.39,7.6,102,5500,24,30,7957 26 | 1,148,dodge,gas,std,four,hatchback,fwd,front,93.7,157.3,63.8,50.6,1967,ohc,four,90,2bbl,2.97,3.23,9.4,68,5500,31,38,6229 27 | 1,148,dodge,gas,std,four,sedan,fwd,front,93.7,157.3,63.8,50.6,1989,ohc,four,90,2bbl,2.97,3.23,9.4,68,5500,31,38,6692 28 | 1,148,dodge,gas,std,four,sedan,fwd,front,93.7,157.3,63.8,50.6,1989,ohc,four,90,2bbl,2.97,3.23,9.4,68,5500,31,38,7609 29 | 1,148,dodge,gas,turbo,?,sedan,fwd,front,93.7,157.3,63.8,50.6,2191,ohc,four,98,mpfi,3.03,3.39,7.6,102,5500,24,30,8558 30 | -1,110,dodge,gas,std,four,wagon,fwd,front,103.3,174.6,64.6,59.8,2535,ohc,four,122,2bbl,3.34,3.46,8.5,88,5000,24,30,8921 31 | 3,145,dodge,gas,turbo,two,hatchback,fwd,front,95.9,173.2,66.3,50.2,2811,ohc,four,156,mfi,3.6,3.9,7,145,5000,19,24,12964 32 | 2,137,honda,gas,std,two,hatchback,fwd,front,86.6,144.6,63.9,50.8,1713,ohc,four,92,1bbl,2.91,3.41,9.6,58,4800,49,54,6479 33 | 2,137,honda,gas,std,two,hatchback,fwd,front,86.6,144.6,63.9,50.8,1819,ohc,four,92,1bbl,2.91,3.41,9.2,76,6000,31,38,6855 34 | 1,101,honda,gas,std,two,hatchback,fwd,front,93.7,150,64,52.6,1837,ohc,four,79,1bbl,2.91,3.07,10.1,60,5500,38,42,5399 35 | 1,101,honda,gas,std,two,hatchback,fwd,front,93.7,150,64,52.6,1940,ohc,four,92,1bbl,2.91,3.41,9.2,76,6000,30,34,6529 36 | 1,101,honda,gas,std,two,hatchback,fwd,front,93.7,150,64,52.6,1956,ohc,four,92,1bbl,2.91,3.41,9.2,76,6000,30,34,7129 37 | 0,110,honda,gas,std,four,sedan,fwd,front,96.5,163.4,64,54.5,2010,ohc,four,92,1bbl,2.91,3.41,9.2,76,6000,30,34,7295 38 | 0,78,honda,gas,std,four,wagon,fwd,front,96.5,157.1,63.9,58.3,2024,ohc,four,92,1bbl,2.92,3.41,9.2,76,6000,30,34,7295 39 | 0,106,honda,gas,std,two,hatchback,fwd,front,96.5,167.5,65.2,53.3,2236,ohc,four,110,1bbl,3.15,3.58,9,86,5800,27,33,7895 40 | 0,106,honda,gas,std,two,hatchback,fwd,front,96.5,167.5,65.2,53.3,2289,ohc,four,110,1bbl,3.15,3.58,9,86,5800,27,33,9095 41 | 0,85,honda,gas,std,four,sedan,fwd,front,96.5,175.4,65.2,54.1,2304,ohc,four,110,1bbl,3.15,3.58,9,86,5800,27,33,8845 42 | 0,85,honda,gas,std,four,sedan,fwd,front,96.5,175.4,62.5,54.1,2372,ohc,four,110,1bbl,3.15,3.58,9,86,5800,27,33,10295 43 | 0,85,honda,gas,std,four,sedan,fwd,front,96.5,175.4,65.2,54.1,2465,ohc,four,110,mpfi,3.15,3.58,9,101,5800,24,28,12945 44 | 1,107,honda,gas,std,two,sedan,fwd,front,96.5,169.1,66,51,2293,ohc,four,110,2bbl,3.15,3.58,9.1,100,5500,25,31,10345 45 | 0,?,isuzu,gas,std,four,sedan,rwd,front,94.3,170.7,61.8,53.5,2337,ohc,four,111,2bbl,3.31,3.23,8.5,78,4800,24,29,6785 46 | 1,?,isuzu,gas,std,two,sedan,fwd,front,94.5,155.9,63.6,52,1874,ohc,four,90,2bbl,3.03,3.11,9.6,70,5400,38,43,? 47 | 0,?,isuzu,gas,std,four,sedan,fwd,front,94.5,155.9,63.6,52,1909,ohc,four,90,2bbl,3.03,3.11,9.6,70,5400,38,43,? 48 | 2,?,isuzu,gas,std,two,hatchback,rwd,front,96,172.6,65.2,51.4,2734,ohc,four,119,spfi,3.43,3.23,9.2,90,5000,24,29,11048 49 | 0,145,jaguar,gas,std,four,sedan,rwd,front,113,199.6,69.6,52.8,4066,dohc,six,258,mpfi,3.63,4.17,8.1,176,4750,15,19,32250 50 | 0,?,jaguar,gas,std,four,sedan,rwd,front,113,199.6,69.6,52.8,4066,dohc,six,258,mpfi,3.63,4.17,8.1,176,4750,15,19,35550 51 | 0,?,jaguar,gas,std,two,sedan,rwd,front,102,191.7,70.6,47.8,3950,ohcv,twelve,326,mpfi,3.54,2.76,11.5,262,5000,13,17,36000 52 | 1,104,mazda,gas,std,two,hatchback,fwd,front,93.1,159.1,64.2,54.1,1890,ohc,four,91,2bbl,3.03,3.15,9,68,5000,30,31,5195 53 | 1,104,mazda,gas,std,two,hatchback,fwd,front,93.1,159.1,64.2,54.1,1900,ohc,four,91,2bbl,3.03,3.15,9,68,5000,31,38,6095 54 | 1,104,mazda,gas,std,two,hatchback,fwd,front,93.1,159.1,64.2,54.1,1905,ohc,four,91,2bbl,3.03,3.15,9,68,5000,31,38,6795 55 | 1,113,mazda,gas,std,four,sedan,fwd,front,93.1,166.8,64.2,54.1,1945,ohc,four,91,2bbl,3.03,3.15,9,68,5000,31,38,6695 56 | 1,113,mazda,gas,std,four,sedan,fwd,front,93.1,166.8,64.2,54.1,1950,ohc,four,91,2bbl,3.08,3.15,9,68,5000,31,38,7395 57 | 3,150,mazda,gas,std,two,hatchback,rwd,front,95.3,169,65.7,49.6,2380,rotor,two,70,4bbl,?,?,9.4,101,6000,17,23,10945 58 | 3,150,mazda,gas,std,two,hatchback,rwd,front,95.3,169,65.7,49.6,2380,rotor,two,70,4bbl,?,?,9.4,101,6000,17,23,11845 59 | 3,150,mazda,gas,std,two,hatchback,rwd,front,95.3,169,65.7,49.6,2385,rotor,two,70,4bbl,?,?,9.4,101,6000,17,23,13645 60 | 3,150,mazda,gas,std,two,hatchback,rwd,front,95.3,169,65.7,49.6,2500,rotor,two,80,mpfi,?,?,9.4,135,6000,16,23,15645 61 | 1,129,mazda,gas,std,two,hatchback,fwd,front,98.8,177.8,66.5,53.7,2385,ohc,four,122,2bbl,3.39,3.39,8.6,84,4800,26,32,8845 62 | 0,115,mazda,gas,std,four,sedan,fwd,front,98.8,177.8,66.5,55.5,2410,ohc,four,122,2bbl,3.39,3.39,8.6,84,4800,26,32,8495 63 | 1,129,mazda,gas,std,two,hatchback,fwd,front,98.8,177.8,66.5,53.7,2385,ohc,four,122,2bbl,3.39,3.39,8.6,84,4800,26,32,10595 64 | 0,115,mazda,gas,std,four,sedan,fwd,front,98.8,177.8,66.5,55.5,2410,ohc,four,122,2bbl,3.39,3.39,8.6,84,4800,26,32,10245 65 | 0,?,mazda,diesel,std,?,sedan,fwd,front,98.8,177.8,66.5,55.5,2443,ohc,four,122,idi,3.39,3.39,22.7,64,4650,36,42,10795 66 | 0,115,mazda,gas,std,four,hatchback,fwd,front,98.8,177.8,66.5,55.5,2425,ohc,four,122,2bbl,3.39,3.39,8.6,84,4800,26,32,11245 67 | 0,118,mazda,gas,std,four,sedan,rwd,front,104.9,175,66.1,54.4,2670,ohc,four,140,mpfi,3.76,3.16,8,120,5000,19,27,18280 68 | 0,?,mazda,diesel,std,four,sedan,rwd,front,104.9,175,66.1,54.4,2700,ohc,four,134,idi,3.43,3.64,22,72,4200,31,39,18344 69 | -1,93,mercedes-benz,diesel,turbo,four,sedan,rwd,front,110,190.9,70.3,56.5,3515,ohc,five,183,idi,3.58,3.64,21.5,123,4350,22,25,25552 70 | -1,93,mercedes-benz,diesel,turbo,four,wagon,rwd,front,110,190.9,70.3,58.7,3750,ohc,five,183,idi,3.58,3.64,21.5,123,4350,22,25,28248 71 | 0,93,mercedes-benz,diesel,turbo,two,hardtop,rwd,front,106.7,187.5,70.3,54.9,3495,ohc,five,183,idi,3.58,3.64,21.5,123,4350,22,25,28176 72 | -1,93,mercedes-benz,diesel,turbo,four,sedan,rwd,front,115.6,202.6,71.7,56.3,3770,ohc,five,183,idi,3.58,3.64,21.5,123,4350,22,25,31600 73 | -1,?,mercedes-benz,gas,std,four,sedan,rwd,front,115.6,202.6,71.7,56.5,3740,ohcv,eight,234,mpfi,3.46,3.1,8.3,155,4750,16,18,34184 74 | 3,142,mercedes-benz,gas,std,two,convertible,rwd,front,96.6,180.3,70.5,50.8,3685,ohcv,eight,234,mpfi,3.46,3.1,8.3,155,4750,16,18,35056 75 | 0,?,mercedes-benz,gas,std,four,sedan,rwd,front,120.9,208.1,71.7,56.7,3900,ohcv,eight,308,mpfi,3.8,3.35,8,184,4500,14,16,40960 76 | 1,?,mercedes-benz,gas,std,two,hardtop,rwd,front,112,199.2,72,55.4,3715,ohcv,eight,304,mpfi,3.8,3.35,8,184,4500,14,16,45400 77 | 1,?,mercury,gas,turbo,two,hatchback,rwd,front,102.7,178.4,68,54.8,2910,ohc,four,140,mpfi,3.78,3.12,8,175,5000,19,24,16503 78 | 2,161,mitsubishi,gas,std,two,hatchback,fwd,front,93.7,157.3,64.4,50.8,1918,ohc,four,92,2bbl,2.97,3.23,9.4,68,5500,37,41,5389 79 | 2,161,mitsubishi,gas,std,two,hatchback,fwd,front,93.7,157.3,64.4,50.8,1944,ohc,four,92,2bbl,2.97,3.23,9.4,68,5500,31,38,6189 80 | 2,161,mitsubishi,gas,std,two,hatchback,fwd,front,93.7,157.3,64.4,50.8,2004,ohc,four,92,2bbl,2.97,3.23,9.4,68,5500,31,38,6669 81 | 1,161,mitsubishi,gas,turbo,two,hatchback,fwd,front,93,157.3,63.8,50.8,2145,ohc,four,98,spdi,3.03,3.39,7.6,102,5500,24,30,7689 82 | 3,153,mitsubishi,gas,turbo,two,hatchback,fwd,front,96.3,173,65.4,49.4,2370,ohc,four,110,spdi,3.17,3.46,7.5,116,5500,23,30,9959 83 | 3,153,mitsubishi,gas,std,two,hatchback,fwd,front,96.3,173,65.4,49.4,2328,ohc,four,122,2bbl,3.35,3.46,8.5,88,5000,25,32,8499 84 | 3,?,mitsubishi,gas,turbo,two,hatchback,fwd,front,95.9,173.2,66.3,50.2,2833,ohc,four,156,spdi,3.58,3.86,7,145,5000,19,24,12629 85 | 3,?,mitsubishi,gas,turbo,two,hatchback,fwd,front,95.9,173.2,66.3,50.2,2921,ohc,four,156,spdi,3.59,3.86,7,145,5000,19,24,14869 86 | 3,?,mitsubishi,gas,turbo,two,hatchback,fwd,front,95.9,173.2,66.3,50.2,2926,ohc,four,156,spdi,3.59,3.86,7,145,5000,19,24,14489 87 | 1,125,mitsubishi,gas,std,four,sedan,fwd,front,96.3,172.4,65.4,51.6,2365,ohc,four,122,2bbl,3.35,3.46,8.5,88,5000,25,32,6989 88 | 1,125,mitsubishi,gas,std,four,sedan,fwd,front,96.3,172.4,65.4,51.6,2405,ohc,four,122,2bbl,3.35,3.46,8.5,88,5000,25,32,8189 89 | 1,125,mitsubishi,gas,turbo,four,sedan,fwd,front,96.3,172.4,65.4,51.6,2403,ohc,four,110,spdi,3.17,3.46,7.5,116,5500,23,30,9279 90 | -1,137,mitsubishi,gas,std,four,sedan,fwd,front,96.3,172.4,65.4,51.6,2403,ohc,four,110,spdi,3.17,3.46,7.5,116,5500,23,30,9279 91 | 1,128,nissan,gas,std,two,sedan,fwd,front,94.5,165.3,63.8,54.5,1889,ohc,four,97,2bbl,3.15,3.29,9.4,69,5200,31,37,5499 92 | 1,128,nissan,diesel,std,two,sedan,fwd,front,94.5,165.3,63.8,54.5,2017,ohc,four,103,idi,2.99,3.47,21.9,55,4800,45,50,7099 93 | 1,128,nissan,gas,std,two,sedan,fwd,front,94.5,165.3,63.8,54.5,1918,ohc,four,97,2bbl,3.15,3.29,9.4,69,5200,31,37,6649 94 | 1,122,nissan,gas,std,four,sedan,fwd,front,94.5,165.3,63.8,54.5,1938,ohc,four,97,2bbl,3.15,3.29,9.4,69,5200,31,37,6849 95 | 1,103,nissan,gas,std,four,wagon,fwd,front,94.5,170.2,63.8,53.5,2024,ohc,four,97,2bbl,3.15,3.29,9.4,69,5200,31,37,7349 96 | 1,128,nissan,gas,std,two,sedan,fwd,front,94.5,165.3,63.8,54.5,1951,ohc,four,97,2bbl,3.15,3.29,9.4,69,5200,31,37,7299 97 | 1,128,nissan,gas,std,two,hatchback,fwd,front,94.5,165.6,63.8,53.3,2028,ohc,four,97,2bbl,3.15,3.29,9.4,69,5200,31,37,7799 98 | 1,122,nissan,gas,std,four,sedan,fwd,front,94.5,165.3,63.8,54.5,1971,ohc,four,97,2bbl,3.15,3.29,9.4,69,5200,31,37,7499 99 | 1,103,nissan,gas,std,four,wagon,fwd,front,94.5,170.2,63.8,53.5,2037,ohc,four,97,2bbl,3.15,3.29,9.4,69,5200,31,37,7999 100 | 2,168,nissan,gas,std,two,hardtop,fwd,front,95.1,162.4,63.8,53.3,2008,ohc,four,97,2bbl,3.15,3.29,9.4,69,5200,31,37,8249 101 | 0,106,nissan,gas,std,four,hatchback,fwd,front,97.2,173.4,65.2,54.7,2324,ohc,four,120,2bbl,3.33,3.47,8.5,97,5200,27,34,8949 102 | 0,106,nissan,gas,std,four,sedan,fwd,front,97.2,173.4,65.2,54.7,2302,ohc,four,120,2bbl,3.33,3.47,8.5,97,5200,27,34,9549 103 | 0,128,nissan,gas,std,four,sedan,fwd,front,100.4,181.7,66.5,55.1,3095,ohcv,six,181,mpfi,3.43,3.27,9,152,5200,17,22,13499 104 | 0,108,nissan,gas,std,four,wagon,fwd,front,100.4,184.6,66.5,56.1,3296,ohcv,six,181,mpfi,3.43,3.27,9,152,5200,17,22,14399 105 | 0,108,nissan,gas,std,four,sedan,fwd,front,100.4,184.6,66.5,55.1,3060,ohcv,six,181,mpfi,3.43,3.27,9,152,5200,19,25,13499 106 | 3,194,nissan,gas,std,two,hatchback,rwd,front,91.3,170.7,67.9,49.7,3071,ohcv,six,181,mpfi,3.43,3.27,9,160,5200,19,25,17199 107 | 3,194,nissan,gas,turbo,two,hatchback,rwd,front,91.3,170.7,67.9,49.7,3139,ohcv,six,181,mpfi,3.43,3.27,7.8,200,5200,17,23,19699 108 | 1,231,nissan,gas,std,two,hatchback,rwd,front,99.2,178.5,67.9,49.7,3139,ohcv,six,181,mpfi,3.43,3.27,9,160,5200,19,25,18399 109 | 0,161,peugot,gas,std,four,sedan,rwd,front,107.9,186.7,68.4,56.7,3020,l,four,120,mpfi,3.46,3.19,8.4,97,5000,19,24,11900 110 | 0,161,peugot,diesel,turbo,four,sedan,rwd,front,107.9,186.7,68.4,56.7,3197,l,four,152,idi,3.7,3.52,21,95,4150,28,33,13200 111 | 0,?,peugot,gas,std,four,wagon,rwd,front,114.2,198.9,68.4,58.7,3230,l,four,120,mpfi,3.46,3.19,8.4,97,5000,19,24,12440 112 | 0,?,peugot,diesel,turbo,four,wagon,rwd,front,114.2,198.9,68.4,58.7,3430,l,four,152,idi,3.7,3.52,21,95,4150,25,25,13860 113 | 0,161,peugot,gas,std,four,sedan,rwd,front,107.9,186.7,68.4,56.7,3075,l,four,120,mpfi,3.46,2.19,8.4,95,5000,19,24,15580 114 | 0,161,peugot,diesel,turbo,four,sedan,rwd,front,107.9,186.7,68.4,56.7,3252,l,four,152,idi,3.7,3.52,21,95,4150,28,33,16900 115 | 0,?,peugot,gas,std,four,wagon,rwd,front,114.2,198.9,68.4,56.7,3285,l,four,120,mpfi,3.46,2.19,8.4,95,5000,19,24,16695 116 | 0,?,peugot,diesel,turbo,four,wagon,rwd,front,114.2,198.9,68.4,58.7,3485,l,four,152,idi,3.7,3.52,21,95,4150,25,25,17075 117 | 0,161,peugot,gas,std,four,sedan,rwd,front,107.9,186.7,68.4,56.7,3075,l,four,120,mpfi,3.46,3.19,8.4,97,5000,19,24,16630 118 | 0,161,peugot,diesel,turbo,four,sedan,rwd,front,107.9,186.7,68.4,56.7,3252,l,four,152,idi,3.7,3.52,21,95,4150,28,33,17950 119 | 0,161,peugot,gas,turbo,four,sedan,rwd,front,108,186.7,68.3,56,3130,l,four,134,mpfi,3.61,3.21,7,142,5600,18,24,18150 120 | 1,119,plymouth,gas,std,two,hatchback,fwd,front,93.7,157.3,63.8,50.8,1918,ohc,four,90,2bbl,2.97,3.23,9.4,68,5500,37,41,5572 121 | 1,119,plymouth,gas,turbo,two,hatchback,fwd,front,93.7,157.3,63.8,50.8,2128,ohc,four,98,spdi,3.03,3.39,7.6,102,5500,24,30,7957 122 | 1,154,plymouth,gas,std,four,hatchback,fwd,front,93.7,157.3,63.8,50.6,1967,ohc,four,90,2bbl,2.97,3.23,9.4,68,5500,31,38,6229 123 | 1,154,plymouth,gas,std,four,sedan,fwd,front,93.7,167.3,63.8,50.8,1989,ohc,four,90,2bbl,2.97,3.23,9.4,68,5500,31,38,6692 124 | 1,154,plymouth,gas,std,four,sedan,fwd,front,93.7,167.3,63.8,50.8,2191,ohc,four,98,2bbl,2.97,3.23,9.4,68,5500,31,38,7609 125 | -1,74,plymouth,gas,std,four,wagon,fwd,front,103.3,174.6,64.6,59.8,2535,ohc,four,122,2bbl,3.35,3.46,8.5,88,5000,24,30,8921 126 | 3,?,plymouth,gas,turbo,two,hatchback,rwd,front,95.9,173.2,66.3,50.2,2818,ohc,four,156,spdi,3.59,3.86,7,145,5000,19,24,12764 127 | 3,186,porsche,gas,std,two,hatchback,rwd,front,94.5,168.9,68.3,50.2,2778,ohc,four,151,mpfi,3.94,3.11,9.5,143,5500,19,27,22018 128 | 3,?,porsche,gas,std,two,hardtop,rwd,rear,89.5,168.9,65,51.6,2756,ohcf,six,194,mpfi,3.74,2.9,9.5,207,5900,17,25,32528 129 | 3,?,porsche,gas,std,two,hardtop,rwd,rear,89.5,168.9,65,51.6,2756,ohcf,six,194,mpfi,3.74,2.9,9.5,207,5900,17,25,34028 130 | 3,?,porsche,gas,std,two,convertible,rwd,rear,89.5,168.9,65,51.6,2800,ohcf,six,194,mpfi,3.74,2.9,9.5,207,5900,17,25,37028 131 | 1,?,porsche,gas,std,two,hatchback,rwd,front,98.4,175.7,72.3,50.5,3366,dohcv,eight,203,mpfi,3.94,3.11,10,288,5750,17,28,? 132 | 0,?,renault,gas,std,four,wagon,fwd,front,96.1,181.5,66.5,55.2,2579,ohc,four,132,mpfi,3.46,3.9,8.7,?,?,23,31,9295 133 | 2,?,renault,gas,std,two,hatchback,fwd,front,96.1,176.8,66.6,50.5,2460,ohc,four,132,mpfi,3.46,3.9,8.7,?,?,23,31,9895 134 | 3,150,saab,gas,std,two,hatchback,fwd,front,99.1,186.6,66.5,56.1,2658,ohc,four,121,mpfi,3.54,3.07,9.31,110,5250,21,28,11850 135 | 2,104,saab,gas,std,four,sedan,fwd,front,99.1,186.6,66.5,56.1,2695,ohc,four,121,mpfi,3.54,3.07,9.3,110,5250,21,28,12170 136 | 3,150,saab,gas,std,two,hatchback,fwd,front,99.1,186.6,66.5,56.1,2707,ohc,four,121,mpfi,2.54,2.07,9.3,110,5250,21,28,15040 137 | 2,104,saab,gas,std,four,sedan,fwd,front,99.1,186.6,66.5,56.1,2758,ohc,four,121,mpfi,3.54,3.07,9.3,110,5250,21,28,15510 138 | 3,150,saab,gas,turbo,two,hatchback,fwd,front,99.1,186.6,66.5,56.1,2808,dohc,four,121,mpfi,3.54,3.07,9,160,5500,19,26,18150 139 | 2,104,saab,gas,turbo,four,sedan,fwd,front,99.1,186.6,66.5,56.1,2847,dohc,four,121,mpfi,3.54,3.07,9,160,5500,19,26,18620 140 | 2,83,subaru,gas,std,two,hatchback,fwd,front,93.7,156.9,63.4,53.7,2050,ohcf,four,97,2bbl,3.62,2.36,9,69,4900,31,36,5118 141 | 2,83,subaru,gas,std,two,hatchback,fwd,front,93.7,157.9,63.6,53.7,2120,ohcf,four,108,2bbl,3.62,2.64,8.7,73,4400,26,31,7053 142 | 2,83,subaru,gas,std,two,hatchback,4wd,front,93.3,157.3,63.8,55.7,2240,ohcf,four,108,2bbl,3.62,2.64,8.7,73,4400,26,31,7603 143 | 0,102,subaru,gas,std,four,sedan,fwd,front,97.2,172,65.4,52.5,2145,ohcf,four,108,2bbl,3.62,2.64,9.5,82,4800,32,37,7126 144 | 0,102,subaru,gas,std,four,sedan,fwd,front,97.2,172,65.4,52.5,2190,ohcf,four,108,2bbl,3.62,2.64,9.5,82,4400,28,33,7775 145 | 0,102,subaru,gas,std,four,sedan,fwd,front,97.2,172,65.4,52.5,2340,ohcf,four,108,mpfi,3.62,2.64,9,94,5200,26,32,9960 146 | 0,102,subaru,gas,std,four,sedan,4wd,front,97,172,65.4,54.3,2385,ohcf,four,108,2bbl,3.62,2.64,9,82,4800,24,25,9233 147 | 0,102,subaru,gas,turbo,four,sedan,4wd,front,97,172,65.4,54.3,2510,ohcf,four,108,mpfi,3.62,2.64,7.7,111,4800,24,29,11259 148 | 0,89,subaru,gas,std,four,wagon,fwd,front,97,173.5,65.4,53,2290,ohcf,four,108,2bbl,3.62,2.64,9,82,4800,28,32,7463 149 | 0,89,subaru,gas,std,four,wagon,fwd,front,97,173.5,65.4,53,2455,ohcf,four,108,mpfi,3.62,2.64,9,94,5200,25,31,10198 150 | 0,85,subaru,gas,std,four,wagon,4wd,front,96.9,173.6,65.4,54.9,2420,ohcf,four,108,2bbl,3.62,2.64,9,82,4800,23,29,8013 151 | 0,85,subaru,gas,turbo,four,wagon,4wd,front,96.9,173.6,65.4,54.9,2650,ohcf,four,108,mpfi,3.62,2.64,7.7,111,4800,23,23,11694 152 | 1,87,toyota,gas,std,two,hatchback,fwd,front,95.7,158.7,63.6,54.5,1985,ohc,four,92,2bbl,3.05,3.03,9,62,4800,35,39,5348 153 | 1,87,toyota,gas,std,two,hatchback,fwd,front,95.7,158.7,63.6,54.5,2040,ohc,four,92,2bbl,3.05,3.03,9,62,4800,31,38,6338 154 | 1,74,toyota,gas,std,four,hatchback,fwd,front,95.7,158.7,63.6,54.5,2015,ohc,four,92,2bbl,3.05,3.03,9,62,4800,31,38,6488 155 | 0,77,toyota,gas,std,four,wagon,fwd,front,95.7,169.7,63.6,59.1,2280,ohc,four,92,2bbl,3.05,3.03,9,62,4800,31,37,6918 156 | 0,81,toyota,gas,std,four,wagon,4wd,front,95.7,169.7,63.6,59.1,2290,ohc,four,92,2bbl,3.05,3.03,9,62,4800,27,32,7898 157 | 0,91,toyota,gas,std,four,wagon,4wd,front,95.7,169.7,63.6,59.1,3110,ohc,four,92,2bbl,3.05,3.03,9,62,4800,27,32,8778 158 | 0,91,toyota,gas,std,four,sedan,fwd,front,95.7,166.3,64.4,53,2081,ohc,four,98,2bbl,3.19,3.03,9,70,4800,30,37,6938 159 | 0,91,toyota,gas,std,four,hatchback,fwd,front,95.7,166.3,64.4,52.8,2109,ohc,four,98,2bbl,3.19,3.03,9,70,4800,30,37,7198 160 | 0,91,toyota,diesel,std,four,sedan,fwd,front,95.7,166.3,64.4,53,2275,ohc,four,110,idi,3.27,3.35,22.5,56,4500,34,36,7898 161 | 0,91,toyota,diesel,std,four,hatchback,fwd,front,95.7,166.3,64.4,52.8,2275,ohc,four,110,idi,3.27,3.35,22.5,56,4500,38,47,7788 162 | 0,91,toyota,gas,std,four,sedan,fwd,front,95.7,166.3,64.4,53,2094,ohc,four,98,2bbl,3.19,3.03,9,70,4800,38,47,7738 163 | 0,91,toyota,gas,std,four,hatchback,fwd,front,95.7,166.3,64.4,52.8,2122,ohc,four,98,2bbl,3.19,3.03,9,70,4800,28,34,8358 164 | 0,91,toyota,gas,std,four,sedan,fwd,front,95.7,166.3,64.4,52.8,2140,ohc,four,98,2bbl,3.19,3.03,9,70,4800,28,34,9258 165 | 1,168,toyota,gas,std,two,sedan,rwd,front,94.5,168.7,64,52.6,2169,ohc,four,98,2bbl,3.19,3.03,9,70,4800,29,34,8058 166 | 1,168,toyota,gas,std,two,hatchback,rwd,front,94.5,168.7,64,52.6,2204,ohc,four,98,2bbl,3.19,3.03,9,70,4800,29,34,8238 167 | 1,168,toyota,gas,std,two,sedan,rwd,front,94.5,168.7,64,52.6,2265,dohc,four,98,mpfi,3.24,3.08,9.4,112,6600,26,29,9298 168 | 1,168,toyota,gas,std,two,hatchback,rwd,front,94.5,168.7,64,52.6,2300,dohc,four,98,mpfi,3.24,3.08,9.4,112,6600,26,29,9538 169 | 2,134,toyota,gas,std,two,hardtop,rwd,front,98.4,176.2,65.6,52,2540,ohc,four,146,mpfi,3.62,3.5,9.3,116,4800,24,30,8449 170 | 2,134,toyota,gas,std,two,hardtop,rwd,front,98.4,176.2,65.6,52,2536,ohc,four,146,mpfi,3.62,3.5,9.3,116,4800,24,30,9639 171 | 2,134,toyota,gas,std,two,hatchback,rwd,front,98.4,176.2,65.6,52,2551,ohc,four,146,mpfi,3.62,3.5,9.3,116,4800,24,30,9989 172 | 2,134,toyota,gas,std,two,hardtop,rwd,front,98.4,176.2,65.6,52,2679,ohc,four,146,mpfi,3.62,3.5,9.3,116,4800,24,30,11199 173 | 2,134,toyota,gas,std,two,hatchback,rwd,front,98.4,176.2,65.6,52,2714,ohc,four,146,mpfi,3.62,3.5,9.3,116,4800,24,30,11549 174 | 2,134,toyota,gas,std,two,convertible,rwd,front,98.4,176.2,65.6,53,2975,ohc,four,146,mpfi,3.62,3.5,9.3,116,4800,24,30,17669 175 | -1,65,toyota,gas,std,four,sedan,fwd,front,102.4,175.6,66.5,54.9,2326,ohc,four,122,mpfi,3.31,3.54,8.7,92,4200,29,34,8948 176 | -1,65,toyota,diesel,turbo,four,sedan,fwd,front,102.4,175.6,66.5,54.9,2480,ohc,four,110,idi,3.27,3.35,22.5,73,4500,30,33,10698 177 | -1,65,toyota,gas,std,four,hatchback,fwd,front,102.4,175.6,66.5,53.9,2414,ohc,four,122,mpfi,3.31,3.54,8.7,92,4200,27,32,9988 178 | -1,65,toyota,gas,std,four,sedan,fwd,front,102.4,175.6,66.5,54.9,2414,ohc,four,122,mpfi,3.31,3.54,8.7,92,4200,27,32,10898 179 | -1,65,toyota,gas,std,four,hatchback,fwd,front,102.4,175.6,66.5,53.9,2458,ohc,four,122,mpfi,3.31,3.54,8.7,92,4200,27,32,11248 180 | 3,197,toyota,gas,std,two,hatchback,rwd,front,102.9,183.5,67.7,52,2976,dohc,six,171,mpfi,3.27,3.35,9.3,161,5200,20,24,16558 181 | 3,197,toyota,gas,std,two,hatchback,rwd,front,102.9,183.5,67.7,52,3016,dohc,six,171,mpfi,3.27,3.35,9.3,161,5200,19,24,15998 182 | -1,90,toyota,gas,std,four,sedan,rwd,front,104.5,187.8,66.5,54.1,3131,dohc,six,171,mpfi,3.27,3.35,9.2,156,5200,20,24,15690 183 | -1,?,toyota,gas,std,four,wagon,rwd,front,104.5,187.8,66.5,54.1,3151,dohc,six,161,mpfi,3.27,3.35,9.2,156,5200,19,24,15750 184 | 2,122,volkswagen,diesel,std,two,sedan,fwd,front,97.3,171.7,65.5,55.7,2261,ohc,four,97,idi,3.01,3.4,23,52,4800,37,46,7775 185 | 2,122,volkswagen,gas,std,two,sedan,fwd,front,97.3,171.7,65.5,55.7,2209,ohc,four,109,mpfi,3.19,3.4,9,85,5250,27,34,7975 186 | 2,94,volkswagen,diesel,std,four,sedan,fwd,front,97.3,171.7,65.5,55.7,2264,ohc,four,97,idi,3.01,3.4,23,52,4800,37,46,7995 187 | 2,94,volkswagen,gas,std,four,sedan,fwd,front,97.3,171.7,65.5,55.7,2212,ohc,four,109,mpfi,3.19,3.4,9,85,5250,27,34,8195 188 | 2,94,volkswagen,gas,std,four,sedan,fwd,front,97.3,171.7,65.5,55.7,2275,ohc,four,109,mpfi,3.19,3.4,9,85,5250,27,34,8495 189 | 2,94,volkswagen,diesel,turbo,four,sedan,fwd,front,97.3,171.7,65.5,55.7,2319,ohc,four,97,idi,3.01,3.4,23,68,4500,37,42,9495 190 | 2,94,volkswagen,gas,std,four,sedan,fwd,front,97.3,171.7,65.5,55.7,2300,ohc,four,109,mpfi,3.19,3.4,10,100,5500,26,32,9995 191 | 3,?,volkswagen,gas,std,two,convertible,fwd,front,94.5,159.3,64.2,55.6,2254,ohc,four,109,mpfi,3.19,3.4,8.5,90,5500,24,29,11595 192 | 3,256,volkswagen,gas,std,two,hatchback,fwd,front,94.5,165.7,64,51.4,2221,ohc,four,109,mpfi,3.19,3.4,8.5,90,5500,24,29,9980 193 | 0,?,volkswagen,gas,std,four,sedan,fwd,front,100.4,180.2,66.9,55.1,2661,ohc,five,136,mpfi,3.19,3.4,8.5,110,5500,19,24,13295 194 | 0,?,volkswagen,diesel,turbo,four,sedan,fwd,front,100.4,180.2,66.9,55.1,2579,ohc,four,97,idi,3.01,3.4,23,68,4500,33,38,13845 195 | 0,?,volkswagen,gas,std,four,wagon,fwd,front,100.4,183.1,66.9,55.1,2563,ohc,four,109,mpfi,3.19,3.4,9,88,5500,25,31,12290 196 | -2,103,volvo,gas,std,four,sedan,rwd,front,104.3,188.8,67.2,56.2,2912,ohc,four,141,mpfi,3.78,3.15,9.5,114,5400,23,28,12940 197 | -1,74,volvo,gas,std,four,wagon,rwd,front,104.3,188.8,67.2,57.5,3034,ohc,four,141,mpfi,3.78,3.15,9.5,114,5400,23,28,13415 198 | -2,103,volvo,gas,std,four,sedan,rwd,front,104.3,188.8,67.2,56.2,2935,ohc,four,141,mpfi,3.78,3.15,9.5,114,5400,24,28,15985 199 | -1,74,volvo,gas,std,four,wagon,rwd,front,104.3,188.8,67.2,57.5,3042,ohc,four,141,mpfi,3.78,3.15,9.5,114,5400,24,28,16515 200 | -2,103,volvo,gas,turbo,four,sedan,rwd,front,104.3,188.8,67.2,56.2,3045,ohc,four,130,mpfi,3.62,3.15,7.5,162,5100,17,22,18420 201 | -1,74,volvo,gas,turbo,four,wagon,rwd,front,104.3,188.8,67.2,57.5,3157,ohc,four,130,mpfi,3.62,3.15,7.5,162,5100,17,22,18950 202 | -1,95,volvo,gas,std,four,sedan,rwd,front,109.1,188.8,68.9,55.5,2952,ohc,four,141,mpfi,3.78,3.15,9.5,114,5400,23,28,16845 203 | -1,95,volvo,gas,turbo,four,sedan,rwd,front,109.1,188.8,68.8,55.5,3049,ohc,four,141,mpfi,3.78,3.15,8.7,160,5300,19,25,19045 204 | -1,95,volvo,gas,std,four,sedan,rwd,front,109.1,188.8,68.9,55.5,3012,ohcv,six,173,mpfi,3.58,2.87,8.8,134,5500,18,23,21485 205 | -1,95,volvo,diesel,turbo,four,sedan,rwd,front,109.1,188.8,68.9,55.5,3217,ohc,six,145,idi,3.01,3.4,23,106,4800,26,27,22470 206 | -1,95,volvo,gas,turbo,four,sedan,rwd,front,109.1,188.8,68.9,55.5,3062,ohc,four,141,mpfi,3.78,3.15,9.5,114,5400,19,25,22625 207 | -------------------------------------------------------------------------------- /Data/Regression/World_Happiness_Report.csv: -------------------------------------------------------------------------------- 1 | Country,Happiness Rank,Happiness Score,Economy,Family,Health,Freedom,Generosity,Corruption,Dystopia,Job Satisfaction,Region 2 | Norway,1,7.537000179,1.616463184,1.53352356,0.796666503,0.635422587,0.362012237,0.315963835,2.277026653,94.6,Western Europe 3 | Denmark,2,7.521999836,1.482383013,1.551121593,0.792565525,0.626006722,0.355280489,0.400770068,2.313707352,93.5,Western Europe 4 | Iceland,3,7.504000187,1.48063302,1.610574007,0.833552122,0.627162635,0.475540221,0.153526559,2.322715282,94.5,Western Europe 5 | Switzerland,4,7.493999958,1.564979553,1.516911745,0.858131289,0.620070577,0.290549278,0.367007285,2.276716232,93.7,Western Europe 6 | Finland,5,7.468999863,1.443571925,1.540246725,0.80915767,0.617950857,0.245482773,0.382611543,2.430181503,91.2,Western Europe 7 | Netherlands,6,7.376999855,1.503944635,1.428939223,0.810696125,0.585384488,0.47048983,0.282661825,2.294804096,93.8,Western Europe 8 | Canada,7,7.315999985,1.479204416,1.481348991,0.834557652,0.611100912,0.435539722,0.287371516,2.187264442,90.5,North America 9 | New Zealand,8,7.31400013,1.405706048,1.548195124,0.816759706,0.61406213,0.500005126,0.382816702,2.046456337,88.6,Asia-Pacific 10 | Sweden,9,7.28399992,1.494387269,1.478162169,0.830875158,0.612924099,0.385399252,0.384398729,2.097537994,92.7,Western Europe 11 | Australia,10,7.28399992,1.484414935,1.510041952,0.843886793,0.601607382,0.47769925,0.30118373,2.065210819,89.2,Asia-Pacific 12 | Israel,11,7.212999821,1.375382423,1.376289964,0.838404,0.405988604,0.330082655,0.0852421,2.801757336,82.1,Asia-Pacific 13 | Costa Rica,12,7.078999996,1.109706283,1.416403651,0.759509265,0.58013165,0.214613229,0.100106589,2.898639202,89.9,Latin America 14 | Austria,13,7.006000042,1.487097263,1.459944963,0.815328419,0.56776619,0.316472322,0.221060365,2.138506413,95.1,Western Europe 15 | United States,14,6.993000031,1.546259284,1.419920564,0.774286628,0.505740523,0.392578781,0.135638788,2.218113422,85.3,North America 16 | Ireland,15,6.977000237,1.535706639,1.558231115,0.809782624,0.573110342,0.427858323,0.298388153,1.773869038,91.5,Western Europe 17 | Germany,16,6.951000214,1.487923384,1.472520351,0.798950732,0.562511384,0.33626917,0.276731938,2.015769958,90.4,Western Europe 18 | Belgium,17,6.890999794,1.463780761,1.462312698,0.818091869,0.539770722,0.231503338,0.251343131,2.124210358,91.1,Western Europe 19 | Luxembourg,18,6.862999916,1.741943598,1.457583666,0.845089495,0.596627891,0.283180982,0.318834424,1.619512081,93.4,Western Europe 20 | United Kingdom,19,6.714000225,1.44163394,1.49646008,0.805335939,0.508190036,0.492774159,0.265428066,1.704143524,87.4,Western Europe 21 | Chile,20,6.65199995,1.25278461,1.284024954,0.819479704,0.376895279,0.326662421,0.082287982,2.509585857,79.8,Latin America 22 | United Arab Emirates,21,6.647999763,1.626343369,1.266410232,0.726798236,0.60834527,0.360941947,0.324489564,1.734703541,79.8,Asia-Pacific 23 | Brazil,22,6.635000229,1.10735321,1.431306005,0.616552353,0.437453747,0.162349895,0.111092761,2.769267082,79.8,Latin America 24 | Czech Republic,23,6.609000206,1.352682352,1.433885217,0.754444003,0.490946174,0.088106759,0.036872927,2.451861858,79.8,Western Europe 25 | Argentina,24,6.598999977,1.185295463,1.440451145,0.695137084,0.494519204,0.109457061,0.059739888,2.614005327,79.8,Latin America 26 | Mexico,25,6.578000069,1.153183818,1.21086216,0.709978998,0.412730008,0.120990433,0.132774115,2.837154865,82.4,Latin America 27 | Singapore,26,6.572000027,1.69227767,1.353814363,0.949492395,0.549840569,0.345965981,0.464307785,1.216362,91,Asia-Pacific 28 | Malta,27,6.52699995,1.343279839,1.488411665,0.821944237,0.588767052,0.574730575,0.153066069,1.556862831,85.2,Western Europe 29 | Uruguay,28,6.453999996,1.217559695,1.412227869,0.719216824,0.579392254,0.175096929,0.178061873,2.172409534,82,Latin America 30 | Guatemala,29,6.453999996,0.872001946,1.255585194,0.54023999,0.531310618,0.283488393,0.077223279,2.893891096,88.1,Latin America 31 | Panama,30,6.452000141,1.233748436,1.373192549,0.706156135,0.550026834,0.210556939,0.070983924,2.307199955,88.3,Latin America 32 | France,31,6.441999912,1.430923462,1.387776852,0.844465852,0.470222116,0.129762307,0.172502428,2.005954742,86,Western Europe 33 | Thailand,32,6.423999786,1.127868772,1.425792456,0.647239029,0.580200732,0.57212311,0.031612735,2.039508343,93.7,Asia-Pacific 34 | Spain,34,6.402999878,1.384397864,1.532090902,0.8889606,0.40878123,0.190133572,0.070914097,1.92775774,88.1,Western Europe 35 | Qatar,35,6.375,1.870765686,1.27429688,0.710098088,0.604130983,0.33047387,0.439299256,1.14546442,87.8,Asia-Pacific 36 | Colombia,36,6.356999874,1.070622325,1.402182937,0.595027924,0.477487415,0.149014473,0.046668742,2.616068125,82.7,Latin America 37 | Saudi Arabia,37,6.343999863,1.530623555,1.286677599,0.59014833,0.449750572,0.147616014,0.273432255,2.065429688,85.7,Asia-Pacific 38 | Trinidad and Tobago,38,6.168000221,1.361355901,1.380228519,0.519983292,0.518630743,0.325296462,0.008964816,2.053247452,82.1,Latin America 39 | Kuwait,39,6.105000019,1.632952452,1.259698749,0.632105708,0.496337593,0.228289798,0.21515955,1.640425205,87.4,Asia-Pacific 40 | Slovakia,40,6.09800005,1.325393558,1.505059242,0.712732911,0.295817465,0.136544481,0.024210852,2.097776651,81.4,Eastern Europe 41 | Bahrain,41,6.086999893,1.488412261,1.323110461,0.653133035,0.536746919,0.172668487,0.25704217,1.656149387,79.9,Asia-Pacific 42 | Malaysia,42,6.084000111,1.29121542,1.284646034,0.618784428,0.402264982,0.41660893,0.065600708,2.004448891,85,Asia-Pacific 43 | Nicaragua,43,6.071000099,0.737299204,1.28721571,0.653095961,0.447551847,0.301674217,0.130687982,2.513930559,82.3,Latin America 44 | Ecuador,44,6.007999897,1.000820398,1.286168814,0.685636222,0.455198199,0.150112465,0.140134647,2.290352583,83,Latin America 45 | El Salvador,45,6.002999783,0.909784496,1.182125092,0.596018553,0.43245253,0.078257985,0.08998096,2.714593887,80.9,Latin America 46 | Poland,46,5.97300005,1.291787863,1.44571197,0.699475348,0.520342112,0.158465967,0.059307806,1.797722816,86.9,Eastern Europe 47 | Uzbekistan,47,5.971000195,0.786441088,1.54896915,0.498272628,0.658248663,0.415983647,0.246528223,1.816913605,87,Asia-Pacific 48 | Italy,48,5.964000225,1.395066619,1.444923282,0.853144348,0.256450713,0.172789648,0.028028091,1.813312054,85.5,Western Europe 49 | Russia,49,5.962999821,1.281778097,1.469282389,0.547349334,0.373783112,0.052263822,0.032962881,2.205607414,74.6,Eastern Europe 50 | Belize,50,5.955999851,0.907975316,1.081417799,0.450191766,0.547509372,0.240015641,0.096581072,2.631955624,84.1,Latin America 51 | Japan,51,5.920000076,1.416915178,1.436337829,0.913475871,0.505625546,0.120572768,0.163760737,1.363223553,74,Asia-Pacific 52 | Lithuania,52,5.90199995,1.314582348,1.473516107,0.62894994,0.234231785,0.010164657,0.011865643,2.228440523,79.4,Eastern Europe 53 | Algeria,53,5.872000217,1.091864467,1.146217465,0.617584646,0.233335808,0.069436647,0.14609611,2.567603827,68.6,Africa 54 | Latvia,54,5.849999905,1.260748625,1.404714942,0.638566971,0.325707912,0.153074786,0.073842727,1.993655205,82.1,Eastern Europe 55 | South Korea,55,5.837999821,1.401678443,1.128274441,0.900214076,0.257921666,0.206674367,0.063282669,1.880378008,74,Asia-Pacific 56 | Moldova,56,5.837999821,0.72887063,1.251825571,0.589465201,0.240729049,0.208779126,0.010091286,2.807808399,70,Eastern Europe 57 | Romania,57,5.824999809,1.217683911,1.15009129,0.685158312,0.457003742,0.133519918,0.004387901,2.176831484,75.1,Eastern Europe 58 | Bolivia,58,5.822999954,0.833756566,1.227619052,0.47363025,0.558732927,0.225560725,0.060477726,2.443279028,84.7,Latin America 59 | Turkmenistan,59,5.822000027,1.130776763,1.493149161,0.43772608,0.418271929,0.249924988,0.25927034,1.832909822,91.7,Asia-Pacific 60 | Kazakhstan,60,5.818999767,1.28455627,1.384369016,0.606041551,0.437454283,0.201964423,0.119282886,1.784892559,79.5,Asia-Pacific 61 | North Cyprus,61,5.809999943,1.346911311,1.186303377,0.834647238,0.471203625,0.266845703,0.155353352,1.549157619,NaN,Eastern Europe 62 | Slovenia,62,5.757999897,1.341205955,1.452518821,0.790828228,0.572575808,0.242649093,0.045128979,1.313317299,88.5,Eastern Europe 63 | Peru,63,5.715000153,1.035225272,1.218770385,0.630166113,0.450002879,0.126819715,0.047049087,2.20726943,77.4,Latin America 64 | Mauritius,64,5.629000187,1.189395547,1.20956099,0.638007462,0.491247326,0.360933751,0.042181555,1.697583914,86,Africa 65 | Cyprus,65,5.620999813,1.355938077,1.131363273,0.844714701,0.355111539,0.271254301,0.041237976,1.621249199,88.7,Eastern Europe 66 | Estonia,66,5.611000061,1.32087934,1.4766711,0.695168316,0.47913143,0.098890811,0.183248922,1.357508659,77.8,Eastern Europe 67 | Belarus,67,5.568999767,1.15655756,1.444945216,0.637714267,0.295400262,0.155137509,0.156313822,1.723232985,71.6,Eastern Europe 68 | Libya,68,5.525000095,1.101803064,1.35756433,0.52016902,0.46573323,0.152073666,0.09261021,1.835011244,75.8,Africa 69 | Turkey,69,5.5,1.198274374,1.337753177,0.637605608,0.3007406,0.046693042,0.09967158,1.879277945,74.9,Asia-Pacific 70 | Paraguay,70,5.493000031,0.932537317,1.50728488,0.579250693,0.473507792,0.224150658,0.091065913,1.68533349,84.5,Latin America 71 | "Hong Kong S.A.R., China",71,5.472000122,1.551674843,1.262790918,0.943062425,0.490968645,0.374465793,0.293933749,0.554633141,81.5,Asia-Pacific 72 | Philippines,72,5.429999828,0.857699215,1.253917575,0.468009055,0.585214674,0.193513423,0.099331893,1.972604752,81.4,Asia-Pacific 73 | Serbia,73,5.394999981,1.069317579,1.258189797,0.650784671,0.208715528,0.220125884,0.040903781,1.947084427,68.5,Eastern Europe 74 | Jordan,74,5.335999966,0.991012394,1.239088893,0.604590058,0.418421149,0.17217046,0.119803272,1.791176558,80.7,Asia-Pacific 75 | Hungary,75,5.323999882,1.286011934,1.343133092,0.687763453,0.175863519,0.078401662,0.036636937,1.716459274,82.4,Eastern Europe 76 | Jamaica,76,5.31099987,0.925579309,1.368218064,0.641022384,0.474307239,0.233818337,0.055267781,1.612325668,78.1,Latin America 77 | Croatia,77,5.293000221,1.222556233,0.967983007,0.701288521,0.255772293,0.248002976,0.04310311,1.854492426,77.1,Eastern Europe 78 | Kosovo,78,5.278999805,0.951484382,1.137853503,0.54145205,0.260287941,0.319931448,0.057471618,2.010540724,84.7,Eastern Europe 79 | China,79,5.272999763,1.081165791,1.160837412,0.741415501,0.472787708,0.028806841,0.022794275,1.764938593,71.4,Asia-Pacific 80 | Pakistan,80,5.269000053,0.726883531,0.67269069,0.402047783,0.235215262,0.315446019,0.124348067,2.79248929,78.1,Asia-Pacific 81 | Indonesia,81,5.262000084,0.995538592,1.274444699,0.492345721,0.443323463,0.611704588,0.015317135,1.429476976,73.3,Asia-Pacific 82 | Venezuela,82,5.25,1.128431201,1.431337595,0.617144227,0.153997123,0.06501963,0.064491123,1.789463758,90.1,Latin America 83 | Montenegro,83,5.236999989,1.121129036,1.238376498,0.667464674,0.194989055,0.197911024,0.088174194,1.729191542,69.7,Eastern Europe 84 | Morocco,84,5.235000134,0.878114581,0.774864435,0.597710669,0.408158332,0.032209955,0.087763183,2.456189394,64.8,Africa 85 | Azerbaijan,85,5.234000206,1.153601766,1.152400255,0.540775776,0.398155838,0.04526934,0.180987507,1.762481689,69.8,Asia-Pacific 86 | Dominican Republic,86,5.230000019,1.079373837,1.402416706,0.574873745,0.552589834,0.18696785,0.113945253,1.31946516,72.7,Latin America 87 | Greece,87,5.227000237,1.289487481,1.239414573,0.810198903,0.095731251,0,0.043289777,1.749221563,79.2,Eastern Europe 88 | Lebanon,88,5.224999905,1.074987531,1.129624248,0.735081077,0.288515985,0.264450759,0.03751383,1.695073843,72.3,Asia-Pacific 89 | Portugal,89,5.195000172,1.315175295,1.367043018,0.795843542,0.4984653,0.095102713,0.015869452,1.107682705,88.4,Western Europe 90 | Bosnia and Herzegovina,90,5.18200016,0.982409418,1.069335938,0.705186307,0.204403177,0.328867495,0,1.892172575,73.7,Eastern Europe 91 | Honduras,91,5.181000233,0.730573118,1.143944979,0.58256948,0.34807986,0.236188874,0.073345453,2.065811157,83.1,Latin America 92 | Macedonia,92,5.175000191,1.064577937,1.207893014,0.644948184,0.325905979,0.253760964,0.060277794,1.617469311,70.6,Eastern Europe 93 | Somalia,93,5.151000023,0.022643184,0.721151352,0.113989137,0.602126956,0.291631311,0.282410324,3.11748457,79.9,Africa 94 | Vietnam,94,5.073999882,0.788547575,1.277491331,0.652168989,0.571055591,0.234968051,0.087633237,1.462318659,80,Asia-Pacific 95 | Nigeria,95,5.073999882,0.783756256,1.215770483,0.05691573,0.394952565,0.230947196,0.026121566,2.365390539,71.1,Africa 96 | Tajikistan,96,5.040999889,0.524713635,1.271463275,0.529235125,0.471566707,0.248997644,0.146377146,1.84904933,81.3,Asia-Pacific 97 | Kyrgyzstan,98,5.004000187,0.596220076,1.394238591,0.553457797,0.454943389,0.428580374,0.039439179,1.536723137,76,Asia-Pacific 98 | Nepal,99,4.961999893,0.479820192,1.179283261,0.504130781,0.440305948,0.394096166,0.072975546,1.891241074,83.3,Asia-Pacific 99 | Mongolia,100,4.954999924,1.027235866,1.493011236,0.557783484,0.394143969,0.33846423,0.032902289,1.111292362,82.2,Asia-Pacific 100 | South Africa,101,4.828999996,1.054698706,1.384788632,0.18708007,0.479246736,0.13936238,0.072509497,1.510908604,61.3,Africa 101 | Tunisia,102,4.804999828,1.007265806,0.86835146,0.613212049,0.28968069,0.049693357,0.086723149,1.89025116,68.4,Africa 102 | Palestinian Territories,103,4.775000095,0.716249228,1.155647159,0.565666974,0.254711062,0.114173174,0.089282602,1.878890276,69.3,Asia-Pacific 103 | Egypt,104,4.735000134,0.989701807,0.997471392,0.520187259,0.282110155,0.128631443,0.114381365,1.702161074,75.1,Africa 104 | Bulgaria,105,4.714000225,1.161459088,1.434379458,0.70821768,0.289231718,0.113177694,0.011051531,0.996139288,74.9,Eastern Europe 105 | Sierra Leone,106,4.709000111,0.368420929,0.984136045,0.005564754,0.318697691,0.293040901,0.071095176,2.668459892,59.2,Africa 106 | Cameroon,107,4.695000172,0.564305365,0.946018219,0.132892117,0.430388749,0.236298457,0.051306631,2.333645582,64.9,Africa 107 | Iran,108,4.691999912,1.156873107,0.711551249,0.639333189,0.249322608,0.387242913,0.048761073,1.498734951,70.3,Asia-Pacific 108 | Albania,109,4.644000053,0.996192753,0.803685248,0.731159747,0.381498635,0.201312944,0.039864216,1.490441561,73.3,Eastern Europe 109 | Bangladesh,110,4.607999802,0.586682975,0.735131741,0.533241034,0.478356659,0.172255352,0.123717859,1.978736162,77.7,Asia-Pacific 110 | Namibia,111,4.573999882,0.964434326,1.098470807,0.338611811,0.520303547,0.077133745,0.093146972,1.481890202,85.4,Africa 111 | Kenya,112,4.552999973,0.560479462,1.067950726,0.30998835,0.452763766,0.444860309,0.064641319,1.651902199,52,Africa 112 | Mozambique,113,4.550000191,0.23430565,0.870701015,0.106654435,0.480791092,0.322228104,0.179436386,2.355650902,70.8,Africa 113 | Myanmar,114,4.545000076,0.36711055,1.123235941,0.397522569,0.514492035,0.838075161,0.188816205,1.115290403,72,Asia-Pacific 114 | Senegal,115,4.534999847,0.479309022,1.179691911,0.409362853,0.377922267,0.183468893,0.115460448,1.789646149,54.7,Africa 115 | Zambia,116,4.513999939,0.636406779,1.003187299,0.257835895,0.461603492,0.249580145,0.07821355,1.826705456,55.2,Africa 116 | Iraq,117,4.497000217,1.102710485,0.978613198,0.50118047,0.288555533,0.199637264,0.107215755,1.318907261,72,Asia-Pacific 117 | Gabon,118,4.465000153,1.198210239,1.155620217,0.356578588,0.312328577,0.043785378,0.076046787,1.322916269,52.5,Africa 118 | Ethiopia,119,4.460000038,0.339233845,0.864669204,0.353409708,0.408842742,0.31265074,0.165455714,2.015743732,73.7,Africa 119 | Sri Lanka,120,4.440000057,1.009850144,1.259976387,0.625130832,0.561213255,0.490863562,0.073653966,0.419389248,84.4,Asia-Pacific 120 | Armenia,121,4.375999928,0.900596738,1.007483721,0.637524426,0.198303267,0.083488092,0.026674422,1.521499157,50.7,Asia-Pacific 121 | India,122,4.315000057,0.792221248,0.754372597,0.455427617,0.469987005,0.231538489,0.092226885,1.519117117,71.5,Asia-Pacific 122 | Mauritania,123,4.291999817,0.648457289,1.27203083,0.28534928,0.096098043,0.201870024,0.136957005,1.651637316,61.7,Africa 123 | Congo (Brazzaville),124,4.290999889,0.808964252,0.832044363,0.289957434,0.435025871,0.120852128,0.079618134,1.724135637,60,Africa 124 | Georgia,125,4.285999775,0.950612664,0.570614934,0.649546981,0.309410036,0.054008815,0.251666635,1.500137806,55.9,Asia-Pacific 125 | Congo (Kinshasa),126,4.28000021,0.092102349,1.229023457,0.191407025,0.235961348,0.246455833,0.060241356,2.224958658,50.2,Africa 126 | Mali,127,4.190000057,0.476180494,1.281473398,0.169365674,0.306613743,0.183354199,0.104970247,1.668190956,53.7,Africa 127 | Ivory Coast,128,4.179999828,0.603048921,0.90478003,0.04864217,0.447706193,0.20123747,0.130061775,1.844964266,55.3,Africa 128 | Cambodia,129,4.168000221,0.601765096,1.006238341,0.429783404,0.633375823,0.385922968,0.068105951,1.042941093,78.9,Asia-Pacific 129 | Sudan,130,4.138999939,0.659516692,1.21400857,0.290920824,0.014995855,0.182317451,0.08984752,1.68706584,56.7,Africa 130 | Ghana,131,4.119999886,0.667224824,0.873664737,0.295637727,0.423026294,0.256923944,0.02533637,1.577867508,63.1,Africa 131 | Ukraine,132,4.096000195,0.894651949,1.394537568,0.575903952,0.122974776,0.270061463,0.023029471,0.814382315,72.3,Europe 132 | Uganda,133,4.080999851,0.381430715,1.129827738,0.217632607,0.443185955,0.325766057,0.057069719,1.526362658,51,Africa 133 | Burkina Faso,134,4.032000065,0.350227714,1.043280005,0.215844259,0.324367851,0.250864685,0.120328106,1.727212906,56.2,Africa 134 | Niger,135,4.027999878,0.161925331,0.993025005,0.268505007,0.363658696,0.228673846,0.138572946,1.873983383,71.1,Africa 135 | Malawi,136,3.970000029,0.233442038,0.512568831,0.315089583,0.466914654,0.28717047,0.072711654,2.081786156,51.8,Africa 136 | Chad,137,3.936000109,0.438012987,0.953855872,0.041134715,0.162342027,0.21611385,0.053581882,2.071238041,67.9,Africa 137 | Zimbabwe,138,3.875,0.375846535,1.083095908,0.196763754,0.336384207,0.189143494,0.095375381,1.597970247,56.3,Africa 138 | Lesotho,139,3.808000088,0.521021247,1.190095186,0,0.390661299,0.157497272,0.11909464,1.42983532,44.4,Africa 139 | Angola,140,3.795000076,0.85842818,1.10441196,0.049868666,0,0.09792649,0.069720335,1.614482403,71.1,Africa 140 | Afghanistan,141,3.79399991,0.401477218,0.581543326,0.180746779,0.10617952,0.311870933,0.06115783,2.150801182,80,Asia-Pacific 141 | Botswana,142,3.766000032,1.122094154,1.221554995,0.341755509,0.505196333,0.099348448,0.098583199,0.377913713,56.1,Africa 142 | Benin,143,3.657000065,0.431085408,0.435299844,0.209930211,0.425962776,0.207948461,0.060929015,1.885630965,49.3,Africa 143 | Madagascar,144,3.644000053,0.305808693,0.913020372,0.375223309,0.189196765,0.20873253,0.067231975,1.584612608,45.3,Africa 144 | Haiti,145,3.602999926,0.368610263,0.640449822,0.27732113,0.030369857,0.489203781,0.09987215,1.697167635,48.5,Latin America 145 | Yemen,146,3.592999935,0.591683447,0.935382247,0.310080916,0.249463722,0.104125209,0.056767423,1.345600605,58.9,Asia-Pacific 146 | South Sudan,147,3.59100008,0.397248626,0.601323128,0.163486004,0.147062436,0.285670817,0.116793513,1.879567385,NaN,Africa 147 | Liberia,148,3.532999992,0.119041793,0.872117937,0.229918197,0.332881182,0.266549885,0.038948249,1.673285961,56.6,Africa 148 | Guinea,149,3.506999969,0.24454993,0.791244686,0.194129139,0.348587513,0.264815092,0.110937618,1.552311897,55.1,Africa 149 | Togo,150,3.494999886,0.305444717,0.43188253,0.247105569,0.380426139,0.196896151,0.095665015,1.837229252,44.8,Africa 150 | Rwanda,151,3.470999956,0.368745893,0.945707023,0.326424807,0.581843853,0.252756029,0.455220014,0.540061235,51.7,Africa 151 | Syria,152,3.461999893,0.777153134,0.396102607,0.500533342,0.081539445,0.493663728,0.151347131,1.061573505,62.7,Asia-Pacific 152 | Tanzania,153,3.348999977,0.511135876,1.041989803,0.364509284,0.390017778,0.354256362,0.066035107,0.621130466,57.8,Africa 153 | Burundi,154,2.904999971,0.091622569,0.629793584,0.151610792,0.059900753,0.204435185,0.084147945,1.683024168,54.3,Africa 154 | Central African Republic,155,2.693000078,0,0,0.018772686,0.270842046,0.280876487,0.056565076,2.066004753,70.4,Africa -------------------------------------------------------------------------------- /Data/classification/Social_Network_Ads.csv: -------------------------------------------------------------------------------- 1 | User ID,Gender,Age,Estimated_Salary,Purchased 2 | 15624510,Male,19,19000,0 3 | 15810944,Male,35,20000,0 4 | 15668575,Female,26,43000,0 5 | 15603246,Female,27,57000,0 6 | 15804002,Male,19,76000,0 7 | 15728773,Male,27,58000,0 8 | 15598044,Female,27,84000,0 9 | 15694829,Female,32,150000,1 10 | 15600575,Male,25,33000,0 11 | 15727311,Female,35,65000,0 12 | 15570769,Female,26,80000,0 13 | 15606274,Female,26,52000,0 14 | 15746139,Male,20,86000,0 15 | 15704987,Male,32,18000,0 16 | 15628972,Male,18,82000,0 17 | 15697686,Male,29,80000,0 18 | 15733883,Male,47,25000,1 19 | 15617482,Male,45,26000,1 20 | 15704583,Male,46,28000,1 21 | 15621083,Female,48,29000,1 22 | 15649487,Male,45,22000,1 23 | 15736760,Female,47,49000,1 24 | 15714658,Male,48,41000,1 25 | 15599081,Female,45,22000,1 26 | 15705113,Male,46,23000,1 27 | 15631159,Male,47,20000,1 28 | 15792818,Male,49,28000,1 29 | 15633531,Female,47,30000,1 30 | 15744529,Male,29,43000,0 31 | 15669656,Male,31,18000,0 32 | 15581198,Male,31,74000,0 33 | 15729054,Female,27,137000,1 34 | 15573452,Female,21,16000,0 35 | 15776733,Female,28,44000,0 36 | 15724858,Male,27,90000,0 37 | 15713144,Male,35,27000,0 38 | 15690188,Female,33,28000,0 39 | 15689425,Male,30,49000,0 40 | 15671766,Female,26,72000,0 41 | 15782806,Female,27,31000,0 42 | 15764419,Female,27,17000,0 43 | 15591915,Female,33,51000,0 44 | 15772798,Male,35,108000,0 45 | 15792008,Male,30,15000,0 46 | 15715541,Female,28,84000,0 47 | 15639277,Male,23,20000,0 48 | 15798850,Male,25,79000,0 49 | 15776348,Female,27,54000,0 50 | 15727696,Male,30,135000,1 51 | 15793813,Female,31,89000,0 52 | 15694395,Female,24,32000,0 53 | 15764195,Female,18,44000,0 54 | 15744919,Female,29,83000,0 55 | 15671655,Female,35,23000,0 56 | 15654901,Female,27,58000,0 57 | 15649136,Female,24,55000,0 58 | 15775562,Female,23,48000,0 59 | 15807481,Male,28,79000,0 60 | 15642885,Male,22,18000,0 61 | 15789109,Female,32,117000,0 62 | 15814004,Male,27,20000,0 63 | 15673619,Male,25,87000,0 64 | 15595135,Female,23,66000,0 65 | 15583681,Male,32,120000,1 66 | 15605000,Female,59,83000,0 67 | 15718071,Male,24,58000,0 68 | 15679760,Male,24,19000,0 69 | 15654574,Female,23,82000,0 70 | 15577178,Female,22,63000,0 71 | 15595324,Female,31,68000,0 72 | 15756932,Male,25,80000,0 73 | 15726358,Female,24,27000,0 74 | 15595228,Female,20,23000,0 75 | 15782530,Female,33,113000,0 76 | 15592877,Male,32,18000,0 77 | 15651983,Male,34,112000,1 78 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15789863,Male,27,89000,0 110 | 15720943,Male,26,86000,0 111 | 15697997,Female,38,80000,0 112 | 15665416,Female,39,71000,0 113 | 15660200,Female,37,71000,0 114 | 15619653,Male,38,61000,0 115 | 15773447,Male,37,55000,0 116 | 15739160,Male,42,80000,0 117 | 15689237,Male,40,57000,0 118 | 15679297,Male,35,75000,0 119 | 15591433,Male,36,52000,0 120 | 15642725,Male,40,59000,0 121 | 15701962,Male,41,59000,0 122 | 15811613,Female,36,75000,0 123 | 15741049,Male,37,72000,0 124 | 15724423,Female,40,75000,0 125 | 15574305,Male,35,53000,0 126 | 15678168,Female,41,51000,0 127 | 15697020,Female,39,61000,0 128 | 15610801,Male,42,65000,0 129 | 15745232,Male,26,32000,0 130 | 15722758,Male,30,17000,0 131 | 15792102,Female,26,84000,0 132 | 15675185,Male,31,58000,0 133 | 15801247,Male,33,31000,0 134 | 15725660,Male,30,87000,0 135 | 15638963,Female,21,68000,0 136 | 15800061,Female,28,55000,0 137 | 15578006,Male,23,63000,0 138 | 15668504,Female,20,82000,0 139 | 15687491,Male,30,107000,1 140 | 15610403,Female,28,59000,0 141 | 15741094,Male,19,25000,0 142 | 15807909,Male,19,85000,0 143 | 15666141,Female,18,68000,0 144 | 15617134,Male,35,59000,0 145 | 15783029,Male,30,89000,0 146 | 15622833,Female,34,25000,0 147 | 15746422,Female,24,89000,0 148 | 15750839,Female,27,96000,1 149 | 15749130,Female,41,30000,0 150 | 15779862,Male,29,61000,0 151 | 15767871,Male,20,74000,0 152 | 15679651,Female,26,15000,0 153 | 15576219,Male,41,45000,0 154 | 15699247,Male,31,76000,0 155 | 15619087,Female,36,50000,0 156 | 15605327,Male,40,47000,0 157 | 15610140,Female,31,15000,0 158 | 15791174,Male,46,59000,0 159 | 15602373,Male,29,75000,0 160 | 15762605,Male,26,30000,0 161 | 15598840,Female,32,135000,1 162 | 15744279,Male,32,100000,1 163 | 15670619,Male,25,90000,0 164 | 15599533,Female,37,33000,0 165 | 15757837,Male,35,38000,0 166 | 15697574,Female,33,69000,0 167 | 15578738,Female,18,86000,0 168 | 15762228,Female,22,55000,0 169 | 15614827,Female,35,71000,0 170 | 15789815,Male,29,148000,1 171 | 15579781,Female,29,47000,0 172 | 15587013,Male,21,88000,0 173 | 15570932,Male,34,115000,0 174 | 15794661,Female,26,118000,0 175 | 15581654,Female,34,43000,0 176 | 15644296,Female,34,72000,0 177 | 15614420,Female,23,28000,0 178 | 15609653,Female,35,47000,0 179 | 15594577,Male,25,22000,0 180 | 15584114,Male,24,23000,0 181 | 15673367,Female,31,34000,0 182 | 15685576,Male,26,16000,0 183 | 15774727,Female,31,71000,0 184 | 15694288,Female,32,117000,1 185 | 15603319,Male,33,43000,0 186 | 15759066,Female,33,60000,0 187 | 15814816,Male,31,66000,0 188 | 15724402,Female,20,82000,0 189 | 15571059,Female,33,41000,0 190 | 15674206,Male,35,72000,0 191 | 15715160,Male,28,32000,0 192 | 15730448,Male,24,84000,0 193 | 15662067,Female,19,26000,0 194 | 15779581,Male,29,43000,0 195 | 15662901,Male,19,70000,0 196 | 15689751,Male,28,89000,0 197 | 15667742,Male,34,43000,0 198 | 15738448,Female,30,79000,0 199 | 15680243,Female,20,36000,0 200 | 15745083,Male,26,80000,0 201 | 15708228,Male,35,22000,0 202 | 15628523,Male,35,39000,0 203 | 15708196,Male,49,74000,0 204 | 15735549,Female,39,134000,1 205 | 15809347,Female,41,71000,0 206 | 15660866,Female,58,101000,1 207 | 15766609,Female,47,47000,0 208 | 15654230,Female,55,130000,1 209 | 15794566,Female,52,114000,0 210 | 15800890,Female,40,142000,1 211 | 15697424,Female,46,22000,0 212 | 15724536,Female,48,96000,1 213 | 15735878,Male,52,150000,1 214 | 15707596,Female,59,42000,0 215 | 15657163,Male,35,58000,0 216 | 15622478,Male,47,43000,0 217 | 15779529,Female,60,108000,1 218 | 15636023,Male,49,65000,0 219 | 15582066,Male,40,78000,0 220 | 15666675,Female,46,96000,0 221 | 15732987,Male,59,143000,1 222 | 15789432,Female,41,80000,0 223 | 15663161,Male,35,91000,1 224 | 15694879,Male,37,144000,1 225 | 15593715,Male,60,102000,1 226 | 15575002,Female,35,60000,0 227 | 15622171,Male,37,53000,0 228 | 15795224,Female,36,126000,1 229 | 15685346,Male,56,133000,1 230 | 15691808,Female,40,72000,0 231 | 15721007,Female,42,80000,1 232 | 15794253,Female,35,147000,1 233 | 15694453,Male,39,42000,0 234 | 15813113,Male,40,107000,1 235 | 15614187,Male,49,86000,1 236 | 15619407,Female,38,112000,0 237 | 15646227,Male,46,79000,1 238 | 15660541,Male,40,57000,0 239 | 15753874,Female,37,80000,0 240 | 15617877,Female,46,82000,0 241 | 15772073,Female,53,143000,1 242 | 15701537,Male,42,149000,1 243 | 15736228,Male,38,59000,0 244 | 15780572,Female,50,88000,1 245 | 15769596,Female,56,104000,1 246 | 15586996,Female,41,72000,0 247 | 15722061,Female,51,146000,1 248 | 15638003,Female,35,50000,0 249 | 15775590,Female,57,122000,1 250 | 15730688,Male,41,52000,0 251 | 15753102,Female,35,97000,1 252 | 15810075,Female,44,39000,0 253 | 15723373,Male,37,52000,0 254 | 15795298,Female,48,134000,1 255 | 15584320,Female,37,146000,1 256 | 15724161,Female,50,44000,0 257 | 15750056,Female,52,90000,1 258 | 15609637,Female,41,72000,0 259 | 15794493,Male,40,57000,0 260 | 15569641,Female,58,95000,1 261 | 15815236,Female,45,131000,1 262 | 15811177,Female,35,77000,0 263 | 15680587,Male,36,144000,1 264 | 15672821,Female,55,125000,1 265 | 15767681,Female,35,72000,0 266 | 15600379,Male,48,90000,1 267 | 15801336,Female,42,108000,1 268 | 15721592,Male,40,75000,0 269 | 15581282,Male,37,74000,0 270 | 15746203,Female,47,144000,1 271 | 15583137,Male,40,61000,0 272 | 15680752,Female,43,133000,0 273 | 15688172,Female,59,76000,1 274 | 15791373,Male,60,42000,1 275 | 15589449,Male,39,106000,1 276 | 15692819,Female,57,26000,1 277 | 15727467,Male,57,74000,1 278 | 15734312,Male,38,71000,0 279 | 15764604,Male,49,88000,1 280 | 15613014,Female,52,38000,1 281 | 15759684,Female,50,36000,1 282 | 15609669,Female,59,88000,1 283 | 15685536,Male,35,61000,0 284 | 15750447,Male,37,70000,1 285 | 15663249,Female,52,21000,1 286 | 15638646,Male,48,141000,0 287 | 15734161,Female,37,93000,1 288 | 15631070,Female,37,62000,0 289 | 15761950,Female,48,138000,1 290 | 15649668,Male,41,79000,0 291 | 15713912,Female,37,78000,1 292 | 15586757,Male,39,134000,1 293 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324 | 15674331,Male,41,52000,0 325 | 15619465,Female,48,30000,1 326 | 15575247,Female,48,131000,1 327 | 15695679,Female,41,60000,0 328 | 15713463,Male,41,72000,0 329 | 15785170,Female,42,75000,0 330 | 15796351,Male,36,118000,1 331 | 15639576,Female,47,107000,1 332 | 15693264,Male,38,51000,0 333 | 15589715,Female,48,119000,1 334 | 15769902,Male,42,65000,0 335 | 15587177,Male,40,65000,0 336 | 15814553,Male,57,60000,1 337 | 15601550,Female,36,54000,0 338 | 15664907,Male,58,144000,1 339 | 15612465,Male,35,79000,0 340 | 15810800,Female,38,55000,0 341 | 15665760,Male,39,122000,1 342 | 15588080,Female,53,104000,1 343 | 15776844,Male,35,75000,0 344 | 15717560,Female,38,65000,0 345 | 15629739,Female,47,51000,1 346 | 15729908,Male,47,105000,1 347 | 15716781,Female,41,63000,0 348 | 15646936,Male,53,72000,1 349 | 15768151,Female,54,108000,1 350 | 15579212,Male,39,77000,0 351 | 15721835,Male,38,61000,0 352 | 15800515,Female,38,113000,1 353 | 15591279,Male,37,75000,0 354 | 15587419,Female,42,90000,1 355 | 15750335,Female,37,57000,0 356 | 15699619,Male,36,99000,1 357 | 15606472,Male,60,34000,1 358 | 15778368,Male,54,70000,1 359 | 15671387,Female,41,72000,0 360 | 15573926,Male,40,71000,1 361 | 15709183,Male,42,54000,0 362 | 15577514,Male,43,129000,1 363 | 15778830,Female,53,34000,1 364 | 15768072,Female,47,50000,1 365 | 15768293,Female,42,79000,0 366 | 15654456,Male,42,104000,1 367 | 15807525,Female,59,29000,1 368 | 15574372,Female,58,47000,1 369 | 15671249,Male,46,88000,1 370 | 15779744,Male,38,71000,0 371 | 15624755,Female,54,26000,1 372 | 15611430,Female,60,46000,1 373 | 15774744,Male,60,83000,1 374 | 15629885,Female,39,73000,0 375 | 15708791,Male,59,130000,1 376 | 15793890,Female,37,80000,0 377 | 15646091,Female,46,32000,1 378 | 15596984,Female,46,74000,0 379 | 15800215,Female,42,53000,0 380 | 15577806,Male,41,87000,1 381 | 15749381,Female,58,23000,1 382 | 15683758,Male,42,64000,0 383 | 15670615,Male,48,33000,1 384 | 15715622,Female,44,139000,1 385 | 15707634,Male,49,28000,1 386 | 15806901,Female,57,33000,1 387 | 15775335,Male,56,60000,1 388 | 15724150,Female,49,39000,1 389 | 15627220,Male,39,71000,0 390 | 15672330,Male,47,34000,1 391 | 15668521,Female,48,35000,1 392 | 15807837,Male,48,33000,1 393 | 15592570,Male,47,23000,1 394 | 15748589,Female,45,45000,1 395 | 15635893,Male,60,42000,1 396 | 15757632,Female,39,59000,0 397 | 15691863,Female,46,41000,1 398 | 15706071,Male,51,23000,1 399 | 15654296,Female,50,20000,1 400 | 15755018,Male,36,33000,0 401 | 15594041,Female,49,36000,1 402 | -------------------------------------------------------------------------------- /Data/multiple_arrays.npz: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Ebimsv/Machine_Learning_Course/a1a69e0baa6290f7860f76c2c27833b23a8f1302/Data/multiple_arrays.npz -------------------------------------------------------------------------------- /Data/numpy_data.csv: -------------------------------------------------------------------------------- 1 | 1.0,2.0,3.0 2 | 4.0,5.0,6.0 3 | 7.0,8.0,9.0 -------------------------------------------------------------------------------- /Data/numpy_data.txt: -------------------------------------------------------------------------------- 1 | 1.0 2.0 3.0 2 | 4.0 5.0 6.0 3 | 7.0 8.0 9.0 -------------------------------------------------------------------------------- /Data/numpy_data_with_missing.csv: -------------------------------------------------------------------------------- 1 | 1.0,2.0,3.0 2 | 4.0,,6.0 3 | 7.0,8.0,9.0 -------------------------------------------------------------------------------- /Data/numpy_output.npy: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Ebimsv/Machine_Learning_Course/a1a69e0baa6290f7860f76c2c27833b23a8f1302/Data/numpy_output.npy -------------------------------------------------------------------------------- /Data/numpy_output.txt: -------------------------------------------------------------------------------- 1 | 1.00,2.00,3.00 2 | 4.00,5.00,6.00 3 | 7.00,8.00,9.00 4 | -------------------------------------------------------------------------------- /Data/pandas/Sales_data.csv: -------------------------------------------------------------------------------- 1 | Order ID,Product Name,Quantity,Price per Unit,Total Sales,Order Date 2 | 1001,Widget A,3,15.0,45.0,2023-01-15 3 | 1002,Widget B,5,22.5,112.5,2023-01-16 4 | 1003,Widget C,2,8.0,16.0,2023-01-17 5 | 1004,Widget A,1,15.0,15.0,2023-01-18 6 | 1005,Widget D,4,30.0,120.0,2023-01-19 7 | -------------------------------------------------------------------------------- /Data/pandas/Sales_data.xlsx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Ebimsv/Machine_Learning_Course/a1a69e0baa6290f7860f76c2c27833b23a8f1302/Data/pandas/Sales_data.xlsx -------------------------------------------------------------------------------- /Data/pandas/sales_data.db: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Ebimsv/Machine_Learning_Course/a1a69e0baa6290f7860f76c2c27833b23a8f1302/Data/pandas/sales_data.db -------------------------------------------------------------------------------- /Data/pandas/sales_data.json: -------------------------------------------------------------------------------- 1 | [ 2 | { 3 | "Order ID": 1001, 4 | "Product Name": "Widget A", 5 | "Quantity": 3, 6 | "Price per Unit": 15.0, 7 | "Total Sales": 45.0, 8 | "Order Date": "2023-01-15" 9 | }, 10 | { 11 | "Order ID": 1002, 12 | "Product Name": "Widget B", 13 | "Quantity": 5, 14 | "Price per Unit": 22.5, 15 | "Total Sales": 112.5, 16 | "Order Date": "2023-01-16" 17 | }, 18 | { 19 | "Order ID": 1003, 20 | "Product Name": "Widget C", 21 | "Quantity": 2, 22 | "Price per Unit": 8.0, 23 | "Total Sales": 16.0, 24 | "Order Date": "2023-01-17" 25 | }, 26 | { 27 | "Order ID": 1004, 28 | "Product Name": "Widget A", 29 | "Quantity": 1, 30 | "Price per Unit": 15.0, 31 | "Total Sales": 15.0, 32 | "Order Date": "2023-01-18" 33 | }, 34 | { 35 | "Order ID": 1005, 36 | "Product Name": "Widget D", 37 | "Quantity": 4, 38 | "Price per Unit": 30.0, 39 | "Total Sales": 120.0, 40 | "Order Date": "2023-01-19" 41 | } 42 | ] -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2024 Ebi 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice 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 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | ![alt text](https://github.com/Ebimsv/Machine_Learning_Course/blob/main/pics/ML.png) 2 | 3 | # About This Project 4 | 5 | Welcome to my Machine Learning Course Repository! 6 | 7 | This project contains the code and resources for my machine learning course at **Tarbiat Modares University**. Throughout the course, we explore a wide range of topics, including: 8 | 9 | - **Supervised Learning**: Techniques for predicting outcomes based on labeled training data. 10 | - **Unsupervised Learning**: Approaches for analyzing data without labeled responses. 11 | - **Deep Learning**: Advanced methods for modeling complex patterns using neural networks. 12 | 13 | ## Purpose 14 | 15 | The code in this repository is designed to assist students in understanding the fundamental concepts and techniques related to machine learning. Each script includes practical examples and tutorials to facilitate hands-on learning and application to real-world problems. 16 | 17 | ## Technologies Used 18 | 19 | The project is implemented in Python and utilizes the following libraries: 20 | 21 | - **NumPy**: For numerical computations. 22 | - **Pandas**: For data manipulation and analysis. 23 | - **Matplotlib**: For data visualization. 24 | - **Scikit-learn**: For standard machine learning algorithms. 25 | - **PyTorch**: For deep learning applications. 26 | 27 | ## Target Audience 28 | 29 | This repository is primarily aimed at students who are new to machine learning. The code is well-documented, providing explanations and comments to ensure clarity for beginners. 30 | 31 | ## Contribution & Contact 32 | 33 | I created this project to share my knowledge and experiences with those interested in learning about machine learning. I hope you find the resources helpful! 34 | 35 | If you have any questions, suggestions, or feedback, please feel free to reach out to me: 36 | 37 | - [My Personal Website](https://ebimsv.github.io/) 38 | - [LinkedIn Profile](https://www.linkedin.com/in/ebimsv/) 39 | 40 | ## Acknowledgments 41 | 42 | Special thanks to the faculty and peers at Tarbiat Modares University for their support and inspiration in developing this course. 43 | 44 | --- 45 | 46 | **Happy Learning!** 🚀 47 | -------------------------------------------------------------------------------- /README_2.md: -------------------------------------------------------------------------------- 1 | ![alt text](https://github.com/Ebimsv/Machine_Learning_Course/blob/main/pics/ML.png) 2 | 3 | # About This Project 4 | This project contains the code and resources for my machine learning course at **Tarbiat Modares University**. The course covers a variety of machine learning topics, including supervised learning, unsupervised learning, and deep learning. The code in this project is designed to help students learn the concepts and techniques covered in the course, and to practice applying them to real-world problems. 5 | 6 | The code is written in Python and uses the following libraries: NumPy, Pandas, Matplotlib, Scikit-learn, and PyTorch. 7 | The target audience for this project is students who are new to machine learning. The code is well-documented and includes examples and tutorials. 8 | 9 | I created this project to share my knowledge and experience with others who are interested in learning about machine learning. 10 | 11 | I hope this code is helpful to you. If you have any questions or suggestions, please feel free to contact me. 12 | 13 | ## Introduction to Python 14 | Python is a popular programming language for machine learning due to its simplicity and versatility. 15 | In this section, we covered the basics of Python programming, including: 16 | 17 | **Variables and data types** 18 | 19 | **Copy and Deep copy** 20 | 21 |
22 | Operators
23 | 24 | - Arithmetic operators 25 | - Assignment operators 26 | - Comparison operators 27 | - Logical operators 28 | - Bitwise operators 29 | - Membership operators 30 | - Identity operators 31 |
32 | 33 |
34 | Data Types
35 | 36 | - Numeric types(int, float, complex) 37 | - Text type(str) 38 | - Sequence types(list, tuple, range) 39 | - Mapping type(dict) 40 | - Set types(set) 41 | - Boolean type(bool) 42 | - Binary types(bytes) 43 |
44 | 45 |
46 | List
47 | 48 | Different types of creating list 49 | - Using square brackets and comma-separated values 50 | - Using the list() constructor 51 | - Using the range() function 52 | - Using a list comprehension 53 | - Creating an empty list and then adding items 54 | 55 | Indexing and Slicing in list 56 | 57 | List methods 58 | - append() 59 | - insert() 60 | - remove() 61 | - pop() 62 | - sort() 63 | - reverse() 64 | - extend() 65 | - index() 66 | - count() 67 | - clear() 68 | - copy() 69 | - len() 70 | 71 | list characteristics 72 | - Mutable 73 | - Ordered 74 | - Heterogeneous 75 | - Variable length 76 | - Nestable 77 | - Iterable 78 |
79 | 80 |
81 | Control flow statements (if, for, while)
82 | 83 | Several advanced forms of the for loop 84 | - for loop with zip() function 85 | - for loop with enumerate() function 86 | - for loop with dictionary 87 | - for loop in a single line (list comprehension) 88 |
89 | 90 |
91 | Functions
92 | 93 | Parameters and Arguments 94 | - Argument syntax 95 | - Parameters syntax 96 | 97 | function annotations 98 | 99 | lambda function 100 | 101 | Some useful Built-in functions: 102 | - enumerate() 103 | - zip() 104 | - map() 105 | - filter() 106 |
107 | 108 | **Iterables and Iterators** 109 | 110 | **try and except** 111 | 112 | ## Introduction to Numpy 113 | Numpy is a Python library for numerical computing, which provides powerful array operations and linear algebra functions. In this section, we covered the following topics: 114 | 115 | - Creating and manipulating arrays 116 | - Indexing and slicing 117 | 118 |
119 | Basic array operations
120 | 121 | - Basic mathematical operations 122 | - Trigonometric functions 123 | - Exponential and logarithmic functions 124 | - Linear algebra operations(dot product, eigenvalue decomposition, matrix inversion) 125 | - Statistical functions 126 | - Axis 127 | - Reshaping and Transposing 128 | - Random number generation 129 |
130 | 131 | ## Introduction to Pandas 132 | Pandas is a Python library for data manipulation and analysis, which provides powerful tools for handling tabular data. In this section, we covered the following topics: 133 | 134 | - Loading and showing data 135 | - Change index 136 | - df.loc[] vs df.iloc[] 137 | - Sort Dataframe by one column 138 | - Boolean Masking for filtering Dataframe 139 | - Data exploration methods (shape, columns, info, describe, unique, value_counts) 140 |
141 | Data visualization methods
142 | 143 | **For numerical features** 144 | - plot() 145 | - scatter() 146 | - hist() 147 | - boxplot() 148 | 149 | **For categorical features** 150 | - bar() 151 | - pie() 152 | - boxplot() 153 | 154 | **Applying function to pandas Dataframe** 155 | 156 | **Data Transformation** 157 | - Grouping (Groupby) 158 | - Pivoting 159 | - Merging 160 |
161 | 162 | ## Introduction to Matplotlib 163 | Matplotlib is a Python library for data visualization, which provides flexible and customizable plotting functions. In this section, we covered the following topics: 164 | 165 | - Basic plotting functions (line plots, scatter plots, histograms) 166 | - Customizing plots (labels, legends, styles, color, marker, title and xlabel-ylabel) 167 | 168 | ## Gradient Descent 169 | Gradient descent is a fundamental optimization algorithm for finding the minimum of a function. In machine learning, it is commonly used to optimize the parameters of a model. In this section, we covered the following topics: 170 | 171 | - Loss Function 172 | - MAE function 173 | - MSE function 174 | - Plot loss Function 175 | - Optimizer 176 | - The intuition behind gradient descent and How GD works 177 | - The mathematical formulation of gradient descent 178 | - Implementing gradient descent from scratch in Python and plot the result 179 | 180 | ## Linear Regression 181 | Linear regression is a simple yet powerful method for modeling the relationship between a dependent variable and one or more independent variables. In this section, we covered the following topics: 182 | 183 | - The intuition behind linear regression 184 | - Simple linear regression (one input variable) 185 | - Hypothesis function 186 | - The mathematical formulation of linear regression 187 | - Plot Hypothesis function with coef, and intercept which are computed by model 188 | - Implementing linear regression using Numpy and Scikit-learn 189 | - Evaluating model performance (mean squared error, R-squared) 190 | 191 | ## Multiple Linear Regression 192 | Multiple linear regression is an extension of linear regression that can model the relationship between a dependent variable and multiple independent variables. In this section, we covered the following topics: 193 | 194 | - Preprocess and EDA 195 | - Check and handle missing values 196 | - Encoding categorical feature 197 | - Change order of columns 198 | - Rename the column names so that they can be codable 199 | - BoxPlot for Outliers 200 | - Feature selection/Reduction 201 | - The intuition behind multiple linear regression (multiple input variables) 202 | - Ordinary Least Squares: closed-form solution 203 | - SGDRegressor() 204 | - Implementing multiple linear regression with two ways (closed-form solution and SGDRegressor()) using Scikit-learn 205 | 206 | ## Logistic Regression 207 | Logistic regression is a machine learning algorithm used for classification problems, where the target variable is binary or categorical. We covered how to implement logistic regression using Python and Scikit-learn, and how to evaluate the performance of a logistic regression model. In this section, we covered the following topics: 208 | - Model with GridSearchCV 209 | - Plot the decision boundary 210 | 211 | ## Neural Networks 212 | Neural networks are a powerful class of machine learning algorithms that can be used for a wide range of tasks, including image recognition, natural language processing, and speech recognition. We covered the basic concepts of neural networks, including how to create a feedforward neural network using Pytorch. 213 | 214 | ## Decision Tree 215 | A decision tree is a type of supervised machine learning algorithm that is commonly used for classification and regression tasks. It is a tree-like model where each internal node represents a test on an attribute, each branch represents the outcome of the test, and each leaf node represents a class label or a numerical value. 216 | 217 | The decision-making process starts at the root node of the tree and follows a path down to a leaf node, based on the values of the attributes being tested. At each internal node, the algorithm chooses the best attribute to split the data based on some metric, such as information gain or Gini impurity. The goal is to create a tree that can accurately predict the class label or numerical value of new instances based on their attribute values. In this section, we covered the following topics: 218 | - A simple decision tree 219 | - Decision Tree with Random search 220 | - Decision Tree with Grid search (Grid search and random search are two commonly used techniques for hyperparameter tuning in machine learning.) 221 | - Train a Random forest -------------------------------------------------------------------------------- /pics/50_startups.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Ebimsv/Machine_Learning_Course/a1a69e0baa6290f7860f76c2c27833b23a8f1302/pics/50_startups.png -------------------------------------------------------------------------------- /pics/Broadcast_with_scalar.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Ebimsv/Machine_Learning_Course/a1a69e0baa6290f7860f76c2c27833b23a8f1302/pics/Broadcast_with_scalar.png 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