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Learn more about bidirectional Unicode charactersOriginal file line number Diff line number Diff line change @@ -0,0 +1,1253 @@ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## How to build a simple Keras model with Tensorflow backend to predict Churn\n", "\n", "The goal of this tutorial is how to build a simple prediction model based on real life problem using a fictional dataset" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Using TensorFlow backend.\n" ] } ], "source": [ "import numpy as np\n", "import pickle\n", "import pandas as pd\n", "from keras.wrappers.scikit_learn import KerasClassifier\n", "from keras.models import Sequential\n", "from keras.layers import Dense\n", "from sklearn.preprocessing import LabelEncoder, OneHotEncoder\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.preprocessing import StandardScaler" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We are importing from a CSV file a fictional dataset, is important have in mind thi dataset could be gotten from anywhere, quering a database directly and parsing just the relevant features of it." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dataset = pd.read_csv('Churn_Modelling.csv')\n", "X = dataset.iloc[:, 3:13].values\n", "y = dataset.iloc[:, 13].values" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "labelencoder_X_1 = LabelEncoder()\n", "X[:, 1] = labelencoder_X_1.fit_transform(X[:, 1])\n", "labelencoder_X_2 = LabelEncoder()\n", "X[:, 2] = labelencoder_X_2.fit_transform(X[:, 2])\n", "onehotencoder = OneHotEncoder(categorical_features = [1])\n", "X = onehotencoder.fit_transform(X).toarray()\n", "X = X[:, 1:]" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Splitting the dataset into the Training set and Test set\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Feature Scaling\n", "sc = StandardScaler()\n", "X_train = sc.fit_transform(X_train)\n", "X_test = sc.transform(X_test)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def build_classifier():\n", " classifier = Sequential()\n", " classifier.add(Dense(units = 6, kernel_initializer = 'uniform', activation = 'relu', input_dim = 11))\n", " classifier.add(Dense(units = 6, kernel_initializer = 'uniform', activation = 'relu'))\n", " classifier.add(Dense(units = 1, kernel_initializer = 'uniform', activation = 'sigmoid'))\n", " classifier.compile(optimizer='rmsprop', loss = 'binary_crossentropy', metrics = ['accuracy'])\n", " return classifier" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/500\n", "8000/8000 [==============================] - 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0s - loss: 0.3992 - acc: 0.8336 \n", "Epoch 391/500\n", "8000/8000 [==============================] - 0s - loss: 0.3991 - acc: 0.8352 \n", "Epoch 392/500\n", "8000/8000 [==============================] - 0s - loss: 0.3990 - acc: 0.8350 \n", "Epoch 393/500\n", "8000/8000 [==============================] - 0s - loss: 0.3991 - acc: 0.8352 \n", "Epoch 394/500\n", "8000/8000 [==============================] - 0s - loss: 0.3991 - acc: 0.8351 \n", "Epoch 395/500\n", "8000/8000 [==============================] - 0s - loss: 0.3988 - acc: 0.8340 \n", "Epoch 396/500\n", "8000/8000 [==============================] - 0s - loss: 0.3985 - acc: 0.8362 \n", "Epoch 397/500\n", "8000/8000 [==============================] - 0s - loss: 0.3993 - acc: 0.8350 \n", "Epoch 398/500\n", "8000/8000 [==============================] - 0s - loss: 0.3990 - acc: 0.8352 \n", "Epoch 399/500\n", "8000/8000 [==============================] - 0s - loss: 0.3992 - acc: 0.8337 \n", "Epoch 400/500\n", "8000/8000 [==============================] - 0s - loss: 0.3989 - acc: 0.8346 \n", "Epoch 401/500\n", "8000/8000 [==============================] - 0s - loss: 0.3991 - acc: 0.8346 \n", "Epoch 402/500\n", "8000/8000 [==============================] - 0s - loss: 0.3992 - acc: 0.8356 \n", "Epoch 403/500\n", "8000/8000 [==============================] - 0s - loss: 0.3991 - acc: 0.8344 \n", "Epoch 404/500\n", "8000/8000 [==============================] - 0s - loss: 0.3990 - acc: 0.8357 \n", "Epoch 405/500\n", "8000/8000 [==============================] - 0s - loss: 0.3992 - acc: 0.8350 \n", "Epoch 406/500\n", "8000/8000 [==============================] - 0s - loss: 0.3987 - acc: 0.8362 \n", "Epoch 407/500\n", "8000/8000 [==============================] - 0s - loss: 0.3988 - acc: 0.8362 \n", "Epoch 408/500\n", "8000/8000 [==============================] - 0s - loss: 0.3992 - acc: 0.8349 \n", "Epoch 409/500\n", "8000/8000 [==============================] - 0s - loss: 0.3988 - acc: 0.8355 \n", "Epoch 410/500\n", "8000/8000 [==============================] - 0s - loss: 0.3989 - acc: 0.8345 \n", "Epoch 411/500\n", "8000/8000 [==============================] - 0s - loss: 0.3991 - acc: 0.8352 \n", "Epoch 412/500\n", "8000/8000 [==============================] - 0s - loss: 0.3991 - acc: 0.8346 \n", "Epoch 413/500\n", "8000/8000 [==============================] - 0s - loss: 0.3987 - acc: 0.8354 \n", "Epoch 414/500\n", "8000/8000 [==============================] - 0s - loss: 0.3987 - acc: 0.8375 \n", "Epoch 415/500\n", "8000/8000 [==============================] - 0s - loss: 0.3991 - acc: 0.8360 \n", "Epoch 416/500\n", "8000/8000 [==============================] - 0s - loss: 0.3989 - acc: 0.8350 \n", "Epoch 417/500\n", "8000/8000 [==============================] - 0s - loss: 0.3991 - acc: 0.8352 \n", "Epoch 418/500\n", "8000/8000 [==============================] - 0s - loss: 0.3991 - acc: 0.8347 \n", "Epoch 419/500\n", "8000/8000 [==============================] - 0s - loss: 0.3987 - acc: 0.8360 \n", "Epoch 420/500\n", "8000/8000 [==============================] - 0s - loss: 0.3992 - acc: 0.8354 \n", "Epoch 421/500\n", "8000/8000 [==============================] - 0s - loss: 0.3988 - acc: 0.8361 \n", "Epoch 422/500\n", "8000/8000 [==============================] - 0s - loss: 0.3991 - acc: 0.8352 \n", "Epoch 423/500\n", "8000/8000 [==============================] - 0s - loss: 0.3984 - acc: 0.8335 \n", "Epoch 424/500\n", "8000/8000 [==============================] - 0s - loss: 0.3989 - acc: 0.8342 \n", "Epoch 425/500\n", "8000/8000 [==============================] - 0s - loss: 0.3989 - acc: 0.8360 \n", "Epoch 426/500\n", "8000/8000 [==============================] - 0s - loss: 0.3990 - acc: 0.8360 \n", "Epoch 427/500\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "8000/8000 [==============================] - 0s - loss: 0.3990 - acc: 0.8354 \n", "Epoch 428/500\n", "8000/8000 [==============================] - 0s - loss: 0.3989 - acc: 0.8349 \n", "Epoch 429/500\n", "8000/8000 [==============================] - 0s - loss: 0.3991 - acc: 0.8362 \n", "Epoch 430/500\n", "8000/8000 [==============================] - 0s - loss: 0.3991 - acc: 0.8361 \n", "Epoch 431/500\n", "8000/8000 [==============================] - 0s - loss: 0.3992 - acc: 0.8361 \n", "Epoch 432/500\n", "8000/8000 [==============================] - 0s - loss: 0.3994 - acc: 0.8350 \n", "Epoch 433/500\n", "8000/8000 [==============================] - 0s - loss: 0.3989 - acc: 0.8351 \n", "Epoch 434/500\n", "8000/8000 [==============================] - 0s - loss: 0.3990 - acc: 0.8356 \n", "Epoch 435/500\n", "8000/8000 [==============================] - 0s - loss: 0.3986 - acc: 0.8364 \n", "Epoch 436/500\n", "8000/8000 [==============================] - 0s - loss: 0.3984 - acc: 0.8366 \n", "Epoch 437/500\n", "8000/8000 [==============================] - 0s - loss: 0.3991 - acc: 0.8360 \n", "Epoch 438/500\n", "8000/8000 [==============================] - 0s - loss: 0.3988 - acc: 0.8361 \n", "Epoch 439/500\n", "8000/8000 [==============================] - 0s - loss: 0.3990 - acc: 0.8371 \n", "Epoch 440/500\n", "8000/8000 [==============================] - 0s - loss: 0.3990 - acc: 0.8357 \n", "Epoch 441/500\n", "8000/8000 [==============================] - 0s - loss: 0.3993 - acc: 0.8362 \n", "Epoch 442/500\n", "8000/8000 [==============================] - 0s - loss: 0.3991 - acc: 0.8347 \n", "Epoch 443/500\n", "8000/8000 [==============================] - 0s - loss: 0.3987 - acc: 0.8347 \n", "Epoch 444/500\n", "8000/8000 [==============================] - 0s - loss: 0.3987 - acc: 0.8366 \n", "Epoch 445/500\n", "8000/8000 [==============================] - 0s - loss: 0.3989 - acc: 0.8355 \n", "Epoch 446/500\n", "8000/8000 [==============================] - 0s - loss: 0.3991 - acc: 0.8351 \n", "Epoch 447/500\n", "8000/8000 [==============================] - 0s - loss: 0.3988 - acc: 0.8354 \n", "Epoch 448/500\n", "8000/8000 [==============================] - 0s - loss: 0.3987 - acc: 0.8352 \n", "Epoch 449/500\n", "8000/8000 [==============================] - 0s - loss: 0.3989 - acc: 0.8349 \n", "Epoch 450/500\n", "8000/8000 [==============================] - 0s - loss: 0.3990 - acc: 0.8356 \n", "Epoch 451/500\n", "8000/8000 [==============================] - 0s - loss: 0.3989 - acc: 0.8364 \n", "Epoch 452/500\n", "8000/8000 [==============================] - 0s - loss: 0.3991 - acc: 0.8351 \n", "Epoch 453/500\n", "8000/8000 [==============================] - 0s - loss: 0.3991 - acc: 0.8352 \n", "Epoch 454/500\n", "8000/8000 [==============================] - 0s - loss: 0.3991 - acc: 0.8351 \n", "Epoch 455/500\n", "8000/8000 [==============================] - 0s - loss: 0.3987 - acc: 0.8369 \n", "Epoch 456/500\n", "8000/8000 [==============================] - 0s - loss: 0.3991 - acc: 0.8346 \n", "Epoch 457/500\n", "8000/8000 [==============================] - 0s - loss: 0.3992 - acc: 0.8362 \n", "Epoch 458/500\n", "8000/8000 [==============================] - 0s - loss: 0.3990 - acc: 0.8365 \n", "Epoch 459/500\n", "8000/8000 [==============================] - 0s - loss: 0.3992 - acc: 0.8361 \n", "Epoch 460/500\n", "8000/8000 [==============================] - 0s - loss: 0.3988 - acc: 0.8354 \n", "Epoch 461/500\n", "8000/8000 [==============================] - 0s - loss: 0.3992 - acc: 0.8349 \n", "Epoch 462/500\n", "8000/8000 [==============================] - 0s - loss: 0.3983 - acc: 0.8346 \n", "Epoch 463/500\n", "8000/8000 [==============================] - 0s - loss: 0.3990 - acc: 0.8359 \n", "Epoch 464/500\n", "8000/8000 [==============================] - 0s - loss: 0.3993 - acc: 0.8359 \n", "Epoch 465/500\n", "8000/8000 [==============================] - 0s - loss: 0.3990 - acc: 0.8354 \n", "Epoch 466/500\n", "8000/8000 [==============================] - 0s - loss: 0.3987 - acc: 0.8342 \n", "Epoch 467/500\n", "8000/8000 [==============================] - 0s - loss: 0.3987 - acc: 0.8356 \n", "Epoch 468/500\n", "8000/8000 [==============================] - 0s - loss: 0.3984 - acc: 0.8347 \n", "Epoch 469/500\n", "8000/8000 [==============================] - 0s - loss: 0.3990 - acc: 0.8345 \n", "Epoch 470/500\n", "8000/8000 [==============================] - 0s - loss: 0.3991 - acc: 0.8361 \n", "Epoch 471/500\n", "8000/8000 [==============================] - 0s - loss: 0.3991 - acc: 0.8357 \n", "Epoch 472/500\n", "8000/8000 [==============================] - 0s - loss: 0.3992 - acc: 0.8360 \n", "Epoch 473/500\n", "8000/8000 [==============================] - 0s - loss: 0.3985 - acc: 0.8355 \n", "Epoch 474/500\n", "8000/8000 [==============================] - 0s - loss: 0.3988 - acc: 0.8351 \n", "Epoch 475/500\n", "8000/8000 [==============================] - 0s - loss: 0.3992 - acc: 0.8347 \n", "Epoch 476/500\n", "8000/8000 [==============================] - 0s - loss: 0.3993 - acc: 0.8361 \n", "Epoch 477/500\n", "8000/8000 [==============================] - 0s - loss: 0.3988 - acc: 0.8356 \n", "Epoch 478/500\n", "8000/8000 [==============================] - 0s - loss: 0.3988 - acc: 0.8354 \n", "Epoch 479/500\n", "8000/8000 [==============================] - 0s - loss: 0.3991 - acc: 0.8366 \n", "Epoch 480/500\n", "8000/8000 [==============================] - 0s - loss: 0.3992 - acc: 0.8339 \n", "Epoch 481/500\n", "8000/8000 [==============================] - 0s - loss: 0.3991 - acc: 0.8351 \n", "Epoch 482/500\n", "8000/8000 [==============================] - 0s - loss: 0.3993 - acc: 0.8341 \n", "Epoch 483/500\n", "8000/8000 [==============================] - 0s - loss: 0.3988 - acc: 0.8347 \n", "Epoch 484/500\n", "8000/8000 [==============================] - 0s - loss: 0.3979 - acc: 0.8341 \n", "Epoch 485/500\n", "8000/8000 [==============================] - 0s - loss: 0.3992 - acc: 0.8349 \n", "Epoch 486/500\n", "8000/8000 [==============================] - 0s - loss: 0.3986 - acc: 0.8346 \n", "Epoch 487/500\n", "8000/8000 [==============================] - 0s - loss: 0.3988 - acc: 0.8349 \n", "Epoch 488/500\n", "8000/8000 [==============================] - 0s - loss: 0.3989 - acc: 0.8346 \n", "Epoch 489/500\n", "8000/8000 [==============================] - 0s - loss: 0.3992 - acc: 0.8357 \n", "Epoch 490/500\n", "8000/8000 [==============================] - 0s - loss: 0.3992 - acc: 0.8347 \n", "Epoch 491/500\n", "8000/8000 [==============================] - 0s - loss: 0.3988 - acc: 0.8347 \n", "Epoch 492/500\n", "8000/8000 [==============================] - 0s - loss: 0.3989 - acc: 0.8352 \n", "Epoch 493/500\n", "8000/8000 [==============================] - 0s - loss: 0.3989 - acc: 0.8344 \n", "Epoch 494/500\n", "8000/8000 [==============================] - 0s - loss: 0.3991 - acc: 0.8356 \n", "Epoch 495/500\n", "8000/8000 [==============================] - 0s - loss: 0.3995 - acc: 0.8354 \n", "Epoch 496/500\n", "8000/8000 [==============================] - 0s - loss: 0.3990 - acc: 0.8372 \n", "Epoch 497/500\n", "8000/8000 [==============================] - 0s - loss: 0.3988 - acc: 0.8357 \n", "Epoch 498/500\n", "8000/8000 [==============================] - 0s - loss: 0.3990 - acc: 0.8356 \n", "Epoch 499/500\n", "8000/8000 [==============================] - 0s - loss: 0.3992 - acc: 0.8347 \n", "Epoch 500/500\n", "8000/8000 [==============================] - 0s - loss: 0.3992 - acc: 0.8345 \n" ] }, { "data": { "text/plain": [ "<keras.callbacks.History at 0x7fae5d3b5a58>" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "classifier = KerasClassifier(build_fn=build_classifier, batch_size=25, epochs=500)\n", "classifier.fit(X_train, y_train)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Predicting a single new observation\n", "#\n", "# Predict if the customer with the following informations will leave the bank:\n", "# Geography: France\n", "# Credit Score: 600\n", "# Gender: Male\n", "# Age: 40\n", "# Tenure: 3\n", "# Balance: 60000\n", "# Number of Products: 2\n", "# Has Credit Card: Yes\n", "# Is Active Member: Yes\n", "# Estimated Salary: 50000" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1/1 [==============================] - 0s\n" ] } ], "source": [ "single_data = np.array([[0.0, 0, 600, 1, 40, 3, 60000, 2, 1, 1, 50000]])\n", "single_data_transformed = sc.transform(single_data)\n", "new_prediction = classifier.predict(single_data_transformed)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[False]]\n" ] } ], "source": [ "new_prediction = (new_prediction > 0.5)\n", "print(new_prediction)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 2 } This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters. Learn more about bidirectional Unicode charactersOriginal file line number Diff line number Diff line change @@ -0,0 +1,24 @@ backports.weakref==1.0rc1 bleach==1.5.0 cycler==0.10.0 h5py==2.7.0 html5lib==0.9999999 Keras==2.0.6 Markdown==2.6.8 matplotlib==2.0.2 numpy==1.13.1 olefile==0.44 pandas==0.20.3 Pillow==4.2.1 protobuf==3.3.0 pyparsing==2.2.0 python-dateutil==2.6.1 pytz==2017.2 PyYAML==3.12 scikit-learn==0.18.2 scipy==0.19.1 six==1.10.0 sklearn==0.0 tensorflow==1.2.1 Theano==0.9.0 Werkzeug==0.12.2