diff --git a/nnkundan2018@gmail.com/Project.ipynb b/nnkundan2018@gmail.com/Project.ipynb new file mode 100755 index 000000000..b31a848e4 --- /dev/null +++ b/nnkundan2018@gmail.com/Project.ipynb @@ -0,0 +1,1359 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Compare Support Vector Machines to a 3 layer Neural Networks " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# import titanic dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "train_data=pd.read_csv(\"/Users/kundannavneet/Desktop/ml/titanic/train.csv\")\n", + "test_data=pd.read_csv(\"/Users/kundannavneet/Desktop/ml/titanic/test.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
0103Braund, Mr. Owen Harrismale22.010A/5 211717.2500NaNS
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.2833C85C
2313Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.9250NaNS
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123S
4503Allen, Mr. William Henrymale35.0003734508.0500NaNS
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" + ], + "text/plain": [ + " PassengerId Survived Pclass \\\n", + "0 1 0 3 \n", + "1 2 1 1 \n", + "2 3 1 3 \n", + "3 4 1 1 \n", + "4 5 0 3 \n", + "\n", + " Name Sex Age SibSp \\\n", + "0 Braund, Mr. Owen Harris male 22.0 1 \n", + "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n", + "2 Heikkinen, Miss. Laina female 26.0 0 \n", + "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n", + "4 Allen, Mr. William Henry male 35.0 0 \n", + "\n", + " Parch Ticket Fare Cabin Embarked \n", + "0 0 A/5 21171 7.2500 NaN S \n", + "1 0 PC 17599 71.2833 C85 C \n", + "2 0 STON/O2. 3101282 7.9250 NaN S \n", + "3 0 113803 53.1000 C123 S \n", + "4 0 373450 8.0500 NaN S " + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "train_data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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PassengerIdPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
08923Kelly, Mr. Jamesmale34.5003309117.8292NaNQ
18933Wilkes, Mrs. James (Ellen Needs)female47.0103632727.0000NaNS
28942Myles, Mr. Thomas Francismale62.0002402769.6875NaNQ
38953Wirz, Mr. Albertmale27.0003151548.6625NaNS
48963Hirvonen, Mrs. Alexander (Helga E Lindqvist)female22.011310129812.2875NaNS
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" + ], + "text/plain": [ + " PassengerId Pclass Name Sex \\\n", + "0 892 3 Kelly, Mr. James male \n", + "1 893 3 Wilkes, Mrs. James (Ellen Needs) female \n", + "2 894 2 Myles, Mr. Thomas Francis male \n", + "3 895 3 Wirz, Mr. Albert male \n", + "4 896 3 Hirvonen, Mrs. Alexander (Helga E Lindqvist) female \n", + "\n", + " Age SibSp Parch Ticket Fare Cabin Embarked \n", + "0 34.5 0 0 330911 7.8292 NaN Q \n", + "1 47.0 1 0 363272 7.0000 NaN S \n", + "2 62.0 0 0 240276 9.6875 NaN Q \n", + "3 27.0 0 0 315154 8.6625 NaN S \n", + "4 22.0 1 1 3101298 12.2875 NaN S " + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "test_data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# quality data to quantity\n", + "train_data = train_data.replace([\"male\", \"female\"], [0,1])\n", + "train_data = train_data.replace([\"S\", \"C\", \"Q\"], [0,1,2])\n", + "train_data= train_data.fillna(0)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "y = train_data[[\"Survived\"]]\n", + "X = train_data[[\"PassengerId\",\"Pclass\",\"Sex\",\"Age\",\"SibSp\",\"Parch\",\"Fare\",\"Embarked\"]]" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# convert to numpy array for NN\n", + "X = X.astype(np.float32).values\n", + "y = y.astype(np.float32).values" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Data preprocessing for test.csv file" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "test_data = test_data.replace([\"male\", \"female\"], [0,1])\n", + "test_data = test_data.replace([\"S\", \"C\", \"Q\"], [0,1,2])\n", + "test_data= test_data.fillna(0)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "X_Test = test_data[[\"PassengerId\",\"Pclass\",\"Sex\",\"Age\",\"SibSp\",\"Parch\",\"Fare\",\"Embarked\"]]\n", + "X_Test = X_Test.astype(np.float32).values" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "np.random.seed(seed=40)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# split DATASET\n", + "from sklearn.model_selection import train_test_split\n", + "Xtrain, Xtest, ytrain, ytest = train_test_split(X, y, test_size=0.3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 3 LAYER NEURAL NETWORK" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Using TensorFlow backend.\n" + ] + } + ], + "source": [ + "# Keras\n", + "from keras.models import Sequential\n", + "from keras.layers import Dense\n", + "from keras.models import Sequential, load_model\n", + "from keras.layers import Dense, Dropout, BatchNormalization, Activation\n", + "from keras.wrappers.scikit_learn import KerasRegressor" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING: Logging before flag parsing goes to stderr.\n", + "W0825 01:44:47.543767 4590474688 deprecation_wrapper.py:119] From /anaconda2/lib/python2.7/site-packages/keras/backend/tensorflow_backend.py:74: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n", + "\n", + "W0825 01:44:47.589251 4590474688 deprecation_wrapper.py:119] From /anaconda2/lib/python2.7/site-packages/keras/backend/tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n", + "\n", + "W0825 01:44:47.593955 4590474688 deprecation_wrapper.py:119] From /anaconda2/lib/python2.7/site-packages/keras/backend/tensorflow_backend.py:4138: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n", + "\n", + "W0825 01:44:47.754290 4590474688 deprecation_wrapper.py:119] From /anaconda2/lib/python2.7/site-packages/keras/backend/tensorflow_backend.py:133: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.\n", + "\n", + "W0825 01:44:47.802438 4590474688 deprecation.py:506] From /anaconda2/lib/python2.7/site-packages/keras/backend/tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n" + ] + } + ], + "source": [ + "# Model \n", + "model = Sequential()\n", + "#input layer\n", + "model.add(Dense(8, input_shape=(8,)))\n", + "model.add(BatchNormalization())\n", + "model.add(Activation(\"relu\"))\n", + "model.add(Dropout(0.4))\n", + "\n", + "# hidden layers\n", + "model.add(Dense(8))\n", + "model.add(BatchNormalization())\n", + "model.add(Activation(\"sigmoid\"))\n", + "model.add(Dropout(0.4))\n", + " \n", + "model.add(Dense(4))\n", + "model.add(BatchNormalization())\n", + "model.add(Activation(\"sigmoid\"))\n", + "model.add(Dropout(0.4))\n", + " \n", + "model.add(Dense(2, activation=\"sigmoid\"))\n", + " \n", + "# output layer\n", + "model.add(Dense(1, activation='linear'))" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "W0825 01:44:48.159192 4590474688 deprecation_wrapper.py:119] From /anaconda2/lib/python2.7/site-packages/keras/optimizers.py:790: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n", + "\n", + "W0825 01:44:48.199831 4590474688 deprecation_wrapper.py:119] From /anaconda2/lib/python2.7/site-packages/keras/backend/tensorflow_backend.py:3376: The name tf.log is deprecated. Please use tf.math.log instead.\n", + "\n", + "W0825 01:44:48.208709 4590474688 deprecation.py:323] From /anaconda2/lib/python2.7/site-packages/tensorflow/python/ops/nn_impl.py:180: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "Use tf.where in 2.0, which has the same broadcast rule as np.where\n" + ] + } + ], + "source": [ + "# model compile for binary classification\n", + "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/anaconda2/lib/python2.7/site-packages/ipykernel_launcher.py:1: UserWarning: The `nb_epoch` argument in `fit` has been renamed `epochs`.\n", + " \"\"\"Entry point for launching an IPython kernel.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/300\n", + "891/891 [==============================] - 7s 8ms/step - loss: 0.6733 - acc: 0.6072\n", + "Epoch 2/300\n", + "891/891 [==============================] - 0s 339us/step - loss: 0.6728 - acc: 0.6094\n", + "Epoch 3/300\n", + "891/891 [==============================] - 0s 375us/step - loss: 0.6695 - acc: 0.6184\n", + "Epoch 4/300\n", + "891/891 [==============================] - 1s 624us/step - loss: 0.6762 - acc: 0.6139\n", + "Epoch 5/300\n", + "891/891 [==============================] - 0s 502us/step - loss: 0.6772 - acc: 0.6105\n", + "Epoch 6/300\n", + "891/891 [==============================] - 0s 304us/step - loss: 0.6759 - acc: 0.6128\n", + "Epoch 7/300\n", + "891/891 [==============================] - 0s 330us/step - loss: 0.6765 - acc: 0.6061\n", + "Epoch 8/300\n", + "891/891 [==============================] - 0s 392us/step - loss: 0.6770 - acc: 0.6061\n", + "Epoch 9/300\n", + "891/891 [==============================] - 0s 274us/step - loss: 0.6726 - acc: 0.6173\n", + "Epoch 10/300\n", + "891/891 [==============================] - 0s 300us/step - loss: 0.6723 - acc: 0.6117\n", + "Epoch 11/300\n", + "891/891 [==============================] - 0s 366us/step - loss: 0.6680 - acc: 0.6184\n", + "Epoch 12/300\n", + "891/891 [==============================] - 0s 368us/step - loss: 0.6777 - acc: 0.6105\n", + "Epoch 13/300\n", + "891/891 [==============================] - 0s 279us/step - loss: 0.6669 - acc: 0.6173\n", + "Epoch 14/300\n", + "891/891 [==============================] - 0s 499us/step - loss: 0.6775 - acc: 0.6139\n", + "Epoch 15/300\n", + "891/891 [==============================] - 0s 449us/step - loss: 0.6680 - acc: 0.6139\n", + "Epoch 16/300\n", + "891/891 [==============================] - 0s 367us/step - loss: 0.6658 - acc: 0.6162\n", + "Epoch 17/300\n", + "891/891 [==============================] - 0s 328us/step - loss: 0.6660 - acc: 0.6162\n", + "Epoch 18/300\n", + "891/891 [==============================] - 0s 283us/step - loss: 0.6684 - acc: 0.6173\n", + "Epoch 19/300\n", + "891/891 [==============================] - 0s 309us/step - loss: 0.6683 - acc: 0.6184\n", + "Epoch 20/300\n", + "891/891 [==============================] - 0s 330us/step - loss: 0.6687 - acc: 0.6128\n", + "Epoch 21/300\n", + "891/891 [==============================] - 0s 341us/step - loss: 0.6697 - acc: 0.6184\n", + "Epoch 22/300\n", + "891/891 [==============================] - 0s 297us/step - loss: 0.6707 - acc: 0.6184\n", + "Epoch 23/300\n", + "891/891 [==============================] - 0s 324us/step - loss: 0.6700 - acc: 0.6162\n", + "Epoch 24/300\n", + "891/891 [==============================] - 0s 348us/step - loss: 0.6776 - acc: 0.6083\n", + "Epoch 25/300\n", + "891/891 [==============================] - 0s 308us/step - loss: 0.6746 - acc: 0.6162\n", + "Epoch 26/300\n", + "891/891 [==============================] - 0s 291us/step - loss: 0.6639 - acc: 0.6162\n", + "Epoch 27/300\n", + "891/891 [==============================] - 0s 340us/step - loss: 0.6662 - acc: 0.6128\n", + "Epoch 28/300\n", + "891/891 [==============================] - 0s 355us/step - loss: 0.6610 - acc: 0.6184\n", + "Epoch 29/300\n", + "891/891 [==============================] - 0s 289us/step - loss: 0.6650 - acc: 0.6229\n", + "Epoch 30/300\n", + "891/891 [==============================] - 0s 349us/step - loss: 0.6672 - acc: 0.6195\n", + "Epoch 31/300\n", + "891/891 [==============================] - 0s 352us/step - loss: 0.6569 - acc: 0.6207\n", + "Epoch 32/300\n", + "891/891 [==============================] - 0s 377us/step - loss: 0.6610 - acc: 0.6173\n", + "Epoch 33/300\n", + "891/891 [==============================] - 0s 462us/step - loss: 0.6606 - acc: 0.6195\n", + "Epoch 34/300\n", + "891/891 [==============================] - 0s 521us/step - loss: 0.6621 - acc: 0.6229\n", + "Epoch 35/300\n", + "891/891 [==============================] - 0s 409us/step - loss: 0.6642 - acc: 0.6263\n", + "Epoch 36/300\n", + "891/891 [==============================] - 0s 354us/step - loss: 0.6625 - acc: 0.6218\n", + "Epoch 37/300\n", + "891/891 [==============================] - 0s 317us/step - loss: 0.6586 - acc: 0.6184\n", + "Epoch 38/300\n", + "891/891 [==============================] - 0s 274us/step - loss: 0.6595 - acc: 0.6207\n", + "Epoch 39/300\n", + "891/891 [==============================] - 0s 340us/step - loss: 0.6624 - acc: 0.6195\n", + "Epoch 40/300\n", + "891/891 [==============================] - 0s 349us/step - loss: 0.6605 - acc: 0.6229\n", + "Epoch 41/300\n", + "891/891 [==============================] - 0s 285us/step - loss: 0.6640 - acc: 0.6173\n", + "Epoch 42/300\n", + "891/891 [==============================] - 0s 330us/step - loss: 0.6628 - acc: 0.6218\n", + "Epoch 43/300\n", + "891/891 [==============================] - 0s 350us/step - loss: 0.6614 - acc: 0.6229\n", + "Epoch 44/300\n", + "891/891 [==============================] - 0s 517us/step - loss: 0.6572 - acc: 0.6162\n", + "Epoch 45/300\n", + "891/891 [==============================] - 0s 407us/step - loss: 0.6627 - acc: 0.6139\n", + "Epoch 46/300\n", + "891/891 [==============================] - 0s 453us/step - loss: 0.6624 - acc: 0.6207\n", + "Epoch 47/300\n", + "891/891 [==============================] - 0s 309us/step - loss: 0.6601 - acc: 0.6240\n", + "Epoch 48/300\n", + "891/891 [==============================] - 0s 475us/step - loss: 0.6620 - acc: 0.6296\n", + "Epoch 49/300\n", + "891/891 [==============================] - 0s 383us/step - loss: 0.6554 - acc: 0.6285\n", + "Epoch 50/300\n", + "891/891 [==============================] - 0s 325us/step - loss: 0.6522 - acc: 0.6229\n", + "Epoch 51/300\n", + "891/891 [==============================] - 0s 356us/step - loss: 0.6588 - acc: 0.6195\n", + "Epoch 52/300\n", + "891/891 [==============================] - 0s 296us/step - loss: 0.6589 - acc: 0.6162\n", + "Epoch 53/300\n", + "891/891 [==============================] - 0s 291us/step - loss: 0.6575 - acc: 0.6251\n", + "Epoch 54/300\n", + "891/891 [==============================] - 0s 337us/step - loss: 0.6587 - acc: 0.6162\n", + "Epoch 55/300\n", + "891/891 [==============================] - 0s 322us/step - loss: 0.6582 - acc: 0.6162\n", + "Epoch 56/300\n", + "891/891 [==============================] - 0s 352us/step - loss: 0.6615 - acc: 0.6229\n", + "Epoch 57/300\n", + "891/891 [==============================] - 0s 472us/step - loss: 0.6657 - acc: 0.6128\n", + "Epoch 58/300\n", + "891/891 [==============================] - 0s 545us/step - loss: 0.6555 - acc: 0.6263\n", + "Epoch 59/300\n", + "891/891 [==============================] - 0s 504us/step - loss: 0.6600 - acc: 0.6207\n", + "Epoch 60/300\n", + "891/891 [==============================] - 0s 442us/step - loss: 0.6510 - acc: 0.6296\n", + "Epoch 61/300\n", + "891/891 [==============================] - 0s 273us/step - loss: 0.6544 - acc: 0.6251\n", + "Epoch 62/300\n", + "891/891 [==============================] - 0s 310us/step - loss: 0.6491 - acc: 0.6263\n", + "Epoch 63/300\n", + "891/891 [==============================] - 0s 335us/step - loss: 0.6506 - acc: 0.6229\n", + "Epoch 64/300\n", + "891/891 [==============================] - 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acc: 0.7351\n", + "Epoch 251/300\n", + "891/891 [==============================] - 0s 279us/step - loss: 0.5569 - acc: 0.7228\n", + "Epoch 252/300\n", + "891/891 [==============================] - 0s 325us/step - loss: 0.5361 - acc: 0.7508\n", + "Epoch 253/300\n", + "891/891 [==============================] - 0s 323us/step - loss: 0.5641 - acc: 0.7116\n", + "Epoch 254/300\n", + "891/891 [==============================] - 0s 265us/step - loss: 0.5522 - acc: 0.7284\n", + "Epoch 255/300\n", + "891/891 [==============================] - 0s 302us/step - loss: 0.5645 - acc: 0.6992\n", + "Epoch 256/300\n", + "891/891 [==============================] - 0s 333us/step - loss: 0.5451 - acc: 0.7306\n", + "Epoch 257/300\n", + "891/891 [==============================] - 0s 299us/step - loss: 0.5476 - acc: 0.7284\n", + "Epoch 258/300\n", + "891/891 [==============================] - 0s 265us/step - loss: 0.5582 - acc: 0.7160\n", + "Epoch 259/300\n", + "891/891 [==============================] - 0s 328us/step - loss: 0.5634 - acc: 0.7217\n", + "Epoch 260/300\n", + "891/891 [==============================] - 0s 320us/step - loss: 0.5378 - acc: 0.7531\n", + "Epoch 261/300\n", + "891/891 [==============================] - 0s 282us/step - loss: 0.5571 - acc: 0.7093\n", + "Epoch 262/300\n", + "891/891 [==============================] - 0s 293us/step - loss: 0.5508 - acc: 0.7306\n", + "Epoch 263/300\n", + "891/891 [==============================] - 0s 324us/step - loss: 0.5611 - acc: 0.7093\n", + "Epoch 264/300\n", + "891/891 [==============================] - 0s 318us/step - loss: 0.5637 - acc: 0.7127\n", + "Epoch 265/300\n", + "891/891 [==============================] - 0s 261us/step - loss: 0.5474 - acc: 0.7363\n", + "Epoch 266/300\n", + "891/891 [==============================] - 0s 290us/step - loss: 0.5750 - acc: 0.7037\n", + "Epoch 267/300\n", + "891/891 [==============================] - 0s 314us/step - loss: 0.5586 - acc: 0.7273\n", + "Epoch 268/300\n", + "891/891 [==============================] - 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0s 321us/step - loss: 0.5357 - acc: 0.7441\n", + "Epoch 287/300\n", + "891/891 [==============================] - 0s 318us/step - loss: 0.5501 - acc: 0.7295\n", + "Epoch 288/300\n", + "891/891 [==============================] - 0s 301us/step - loss: 0.5335 - acc: 0.7340\n", + "Epoch 289/300\n", + "891/891 [==============================] - 0s 274us/step - loss: 0.5471 - acc: 0.7329\n", + "Epoch 290/300\n", + "891/891 [==============================] - 0s 346us/step - loss: 0.5556 - acc: 0.7194\n", + "Epoch 291/300\n", + "891/891 [==============================] - 0s 338us/step - loss: 0.5569 - acc: 0.7250\n", + "Epoch 292/300\n", + "891/891 [==============================] - 0s 280us/step - loss: 0.5392 - acc: 0.7407\n", + "Epoch 293/300\n", + "891/891 [==============================] - 0s 308us/step - loss: 0.5435 - acc: 0.7273\n", + "Epoch 294/300\n", + "891/891 [==============================] - 0s 330us/step - loss: 0.5776 - acc: 0.7149\n", + "Epoch 295/300\n", + "891/891 [==============================] - 0s 308us/step - loss: 0.5598 - acc: 0.7273\n", + "Epoch 296/300\n", + "891/891 [==============================] - 0s 268us/step - loss: 0.5642 - acc: 0.7273\n", + "Epoch 297/300\n", + "891/891 [==============================] - 0s 308us/step - loss: 0.5311 - acc: 0.7475\n", + "Epoch 298/300\n", + "891/891 [==============================] - 0s 313us/step - loss: 0.5759 - acc: 0.7026\n", + "Epoch 299/300\n", + "891/891 [==============================] - 0s 308us/step - loss: 0.5395 - acc: 0.7419\n", + "Epoch 300/300\n", + "891/891 [==============================] - 0s 262us/step - loss: 0.5450 - acc: 0.7396\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.fit(X, y, nb_epoch=300, batch_size=30)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "268/268 [==============================] - 1s 3ms/step\n" + ] + }, + { + "data": { + "text/plain": [ + "[0.47996548396437916, 0.7835820886626172]" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.evaluate(Xtest,ytest)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "neural_network_model_predictions = np.round(model.predict(X_Test))\n", + "neural_network_model_predictions = pd.DataFrame(neural_network_model_predictions)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "neural_network_result = pd.concat([test_data[[\"PassengerId\"]], neural_network_model_predictions], axis = 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "neural_network_result.to_csv(\"neural_network_result.csv\", index=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# SVM" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.svm import SVC" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "model=SVC()" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/anaconda2/lib/python2.7/site-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", + " y = column_or_1d(y, warn=True)\n", + "/anaconda2/lib/python2.7/site-packages/sklearn/svm/base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n", + " \"avoid this warning.\", FutureWarning)\n" + ] + }, + { + "data": { + "text/plain": [ + "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n", + " decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n", + " kernel='rbf', max_iter=-1, probability=False, random_state=None,\n", + " shrinking=True, tol=0.001, verbose=False)" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.fit(Xtrain,ytrain)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "prediction_svc=model.predict(Xtest)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "svm_accuracy=model.score(Xtest,ytest)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.5895522388059702" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "svm_accuracy" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Conclusion:\n", + "Here, we are getting better accuracy in 3 layer neural network model as compare to support vector machine" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.15" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/nnkundan2018@gmail.com/Project_report.pdf b/nnkundan2018@gmail.com/Project_report.pdf new file mode 100644 index 000000000..3d78f40be Binary files /dev/null and b/nnkundan2018@gmail.com/Project_report.pdf differ