├── Baby_Health.ipynb └── README.md /Baby_Health.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "nbformat": 4, 3 | "nbformat_minor": 0, 4 | "metadata": { 5 | "colab": { 6 | "name": "Baby Health.ipynb", 7 | "provenance": [] 8 | }, 9 | "kernelspec": { 10 | "name": "python3", 11 | "display_name": "Python 3" 12 | }, 13 | "language_info": { 14 | "name": "python" 15 | } 16 | }, 17 | "cells": [ 18 | { 19 | "cell_type": "code", 20 | "execution_count": 68, 21 | "metadata": { 22 | "colab": { 23 | "base_uri": "https://localhost:8080/" 24 | }, 25 | "id": "088NgMejWyOm", 26 | "outputId": "381c7642-f59a-433a-a166-ed25cf4df0d0" 27 | }, 28 | "outputs": [ 29 | { 30 | "output_type": "stream", 31 | "name": "stdout", 32 | "text": [ 33 | "Mounted at /content/drive\n" 34 | ] 35 | } 36 | ], 37 | "source": [ 38 | "#add your code \n", 39 | "from google.colab import drive\n", 40 | "\n", 41 | "drive.mount('/content/drive')" 42 | ] 43 | }, 44 | { 45 | "cell_type": "code", 46 | "source": [ 47 | "import pandas as pd\n", 48 | "df = pd.read_csv('/content/dataset1.csv')" 49 | ], 50 | "metadata": { 51 | "id": "uhqhmK4rokxH" 52 | }, 53 | "execution_count": 69, 54 | "outputs": [] 55 | }, 56 | { 57 | "cell_type": "code", 58 | "source": [ 59 | "df.head()" 60 | ], 61 | "metadata": { 62 | "colab": { 63 | "base_uri": "https://localhost:8080/", 64 | "height": 287 65 | }, 66 | "id": "Rgq3ksewo7Gk", 67 | "outputId": "644d2937-bdee-461e-f116-3bb60ac1df6b" 68 | }, 69 | "execution_count": 70, 70 | "outputs": [ 71 | { 72 | "output_type": "execute_result", 73 | "data": { 74 | "text/html": [ 75 | "\n", 76 | "
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baseline valueaccelerationsfetal_movementuterine_contractionslight_decelerationssevere_decelerationsprolongued_decelerationsabnormal_short_term_variabilitymean_value_of_short_term_variabilitypercentage_of_time_with_abnormal_long_term_variabilitymean_value_of_long_term_variabilityhistogram_widthhistogram_minhistogram_maxhistogram_number_of_peakshistogram_number_of_zeroeshistogram_modehistogram_meanhistogram_medianhistogram_variancehistogram_tendencyfetal_health
0120.00.0000.00.0000.0000.00.073.00.543.02.464.062.0126.02.00.0120.0137.0121.073.01.02.0
1132.00.0060.00.0060.0030.00.017.02.10.010.4130.068.0198.06.01.0141.0136.0140.012.00.01.0
2133.00.0030.00.0080.0030.00.016.02.10.013.4130.068.0198.05.01.0141.0135.0138.013.00.01.0
3134.00.0030.00.0080.0030.00.016.02.40.023.0117.053.0170.011.00.0137.0134.0137.013.01.01.0
4132.00.0070.00.0080.0000.00.016.02.40.019.9117.053.0170.09.00.0137.0136.0138.011.01.01.0
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\n", 324 | " " 325 | ], 326 | "text/plain": [ 327 | " baseline value accelerations ... histogram_tendency fetal_health\n", 328 | "0 120.0 0.000 ... 1.0 2.0\n", 329 | "1 132.0 0.006 ... 0.0 1.0\n", 330 | "2 133.0 0.003 ... 0.0 1.0\n", 331 | "3 134.0 0.003 ... 1.0 1.0\n", 332 | "4 132.0 0.007 ... 1.0 1.0\n", 333 | "\n", 334 | "[5 rows x 22 columns]" 335 | ] 336 | }, 337 | "metadata": {}, 338 | "execution_count": 70 339 | } 340 | ] 341 | }, 342 | { 343 | "cell_type": "code", 344 | "source": [ 345 | "y = pd.get_dummies(df['fetal_health']).values" 346 | ], 347 | "metadata": { 348 | "id": "RwDNE7otbgdl" 349 | }, 350 | "execution_count": 101, 351 | "outputs": [] 352 | }, 353 | { 354 | "cell_type": "code", 355 | "source": [ 356 | "y" 357 | ], 358 | "metadata": { 359 | "colab": { 360 | "base_uri": "https://localhost:8080/" 361 | }, 362 | "id": "7ZcbuB9cbo75", 363 | "outputId": "d76b9164-f8e9-4441-c646-f87d0639bea5" 364 | }, 365 | "execution_count": 109, 366 | "outputs": [ 367 | { 368 | "output_type": "execute_result", 369 | "data": { 370 | "text/plain": [ 371 | "array([[0, 1, 0],\n", 372 | " [1, 0, 0],\n", 373 | " [1, 0, 0],\n", 374 | " ...,\n", 375 | " [0, 1, 0],\n", 376 | " [0, 1, 0],\n", 377 | " [1, 0, 0]], dtype=uint8)" 378 | ] 379 | }, 380 | "metadata": {}, 381 | "execution_count": 109 382 | } 383 | ] 384 | }, 385 | { 386 | "cell_type": "code", 387 | "source": [ 388 | "from sklearn.model_selection import train_test_split\n", 389 | "X_train, X_test, Y_train, Y_test = train_test_split(df.iloc[:,df.columns != 'fetal_health'],y,test_size=.2)" 390 | ], 391 | "metadata": { 392 | "id": "ilGvN7YlTRqQ" 393 | }, 394 | "execution_count": 102, 395 | "outputs": [] 396 | }, 397 | { 398 | "cell_type": "code", 399 | "source": [ 400 | "from sklearn.preprocessing import StandardScaler\n", 401 | "sc = StandardScaler()\n", 402 | "X_train = sc.fit_transform(X_train)\n", 403 | "X_test = sc.transform(X_test)" 404 | ], 405 | "metadata": { 406 | "id": "gZgXP1UDTyq2" 407 | }, 408 | "execution_count": 103, 409 | "outputs": [] 410 | }, 411 | { 412 | "cell_type": "code", 413 | "source": [ 414 | "import numpy as np\n", 415 | "X_train = np.array(X_train)\n", 416 | "X_test = np.array(X_test)\n", 417 | "Y_train=np.array(Y_train)\n", 418 | "Y_test=np.array(Y_test)\n", 419 | "\n", 420 | "X_train = np.reshape(X_train, (X_train.shape[0], 1, X_train.shape[1]))\n", 421 | "X_test = np.reshape(X_test, (X_test.shape[0], 1, X_test.shape[1]))\n", 422 | "Y_train = np.reshape(Y_train, (Y_train.shape[0], 1, Y_train.shape[1]))\n", 423 | "Y_test = np.reshape(Y_test, (Y_test.shape[0], 1, Y_test.shape[1]))" 424 | ], 425 | "metadata": { 426 | "id": "cs13q-g8ZTNR" 427 | }, 428 | "execution_count": 104, 429 | "outputs": [] 430 | }, 431 | { 432 | "cell_type": "code", 433 | "source": [ 434 | "from keras.models import Sequential\n", 435 | "from keras.layers import Dense, Conv1D, MaxPooling1D, Dropout\n", 436 | "\n", 437 | "model = Sequential()\n", 438 | "model.add(Conv1D(filters=32, kernel_size=3, padding='same', activation='relu'))\n", 439 | "model.add(MaxPooling1D(pool_size=1))\n", 440 | "model.add(Conv1D(16,kernel_initializer = 'uniform', activation='relu', kernel_size=1))\n", 441 | "model.add(MaxPooling1D(pool_size=1))\n", 442 | "model.add(Dense(10,kernel_initializer = 'uniform', activation='relu'))\n", 443 | "model.add(Dropout(0.2, input_shape=(10,)))\n", 444 | "model.add(Dense(3, kernel_initializer = 'uniform',activation='sigmoid'))" 445 | ], 446 | "metadata": { 447 | "id": "tL-9kqo-VaJ0" 448 | }, 449 | "execution_count": 106, 450 | "outputs": [] 451 | }, 452 | { 453 | "cell_type": "code", 454 | "source": [ 455 | "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])" 456 | ], 457 | "metadata": { 458 | "id": "-4nsK39EXDd3" 459 | }, 460 | "execution_count": 107, 461 | "outputs": [] 462 | }, 463 | { 464 | "cell_type": "code", 465 | "source": [ 466 | "model.fit(X_train, Y_train, epochs=6, batch_size=32)" 467 | ], 468 | "metadata": { 469 | "colab": { 470 | "base_uri": "https://localhost:8080/" 471 | }, 472 | "id": "9nVbKaSXXSr2", 473 | "outputId": "2d86d140-272b-405e-d5e9-fc2a2ad69601" 474 | }, 475 | "execution_count": 108, 476 | "outputs": [ 477 | { 478 | "output_type": "stream", 479 | "name": "stdout", 480 | "text": [ 481 | "Epoch 1/6\n", 482 | "54/54 [==============================] - 2s 5ms/step - loss: 1.0408 - accuracy: 0.7712\n", 483 | "Epoch 2/6\n", 484 | "54/54 [==============================] - 0s 4ms/step - loss: 0.6398 - accuracy: 0.7788\n", 485 | "Epoch 3/6\n", 486 | "54/54 [==============================] - 0s 4ms/step - loss: 0.4669 - accuracy: 0.7788\n", 487 | "Epoch 4/6\n", 488 | "54/54 [==============================] - 0s 4ms/step - loss: 0.4081 - accuracy: 0.7794\n", 489 | "Epoch 5/6\n", 490 | "54/54 [==============================] - 0s 4ms/step - loss: 0.3842 - accuracy: 0.8188\n", 491 | "Epoch 6/6\n", 492 | "54/54 [==============================] - 0s 4ms/step - loss: 0.3616 - accuracy: 0.8406\n" 493 | ] 494 | }, 495 | { 496 | "output_type": "execute_result", 497 | "data": { 498 | "text/plain": [ 499 | "" 500 | ] 501 | }, 502 | "metadata": {}, 503 | "execution_count": 108 504 | } 505 | ] 506 | }, 507 | { 508 | "cell_type": "code", 509 | "source": [ 510 | "# evaluate the keras model\n", 511 | "_, accuracy = model.evaluate(X_train, Y_train)\n", 512 | "print('Accuracy: %.2f' % (accuracy*100))" 513 | ], 514 | "metadata": { 515 | "colab": { 516 | "base_uri": "https://localhost:8080/" 517 | }, 518 | "id": "E9cRoBsGcUc-", 519 | "outputId": "0bc2d656-d737-4e56-d797-81a8d40326f0" 520 | }, 521 | "execution_count": 112, 522 | "outputs": [ 523 | { 524 | "output_type": "stream", 525 | "name": "stdout", 526 | "text": [ 527 | "54/54 [==============================] - 1s 2ms/step - loss: 0.2551 - accuracy: 0.8512\n", 528 | "Accuracy: 85.12\n" 529 | ] 530 | } 531 | ] 532 | } 533 | ] 534 | } -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | #### Baby Health - Data Science 2 | - Convolution Layer 3 | - Dense Layer 4 | - Neural Network 5 | - Overfitting 6 | - Epochs 7 | - Batch 8 | --------------------------------------------------------------------------------