{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "##![LearnAI Header](https://coursematerial.blob.core.windows.net/assets/LearnAI_header.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "-sandbox\n", "\n", "# Getting started with Machine Learning for Predictive Maintenance\n", "\n", "In this lab, we will create our first Machine Learning solution for predictive maintenance. We will rely on a simple but powerful algorithm: [Logistic Regression](https://en.wikipedia.org/wiki/Logistic_regression)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Reading the data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We begin by reading the data that we finished pre-processing in a prior Notebook.\n", "\n", "> *Note:* If you you do get an error messages about a non-existent file, run the *feature_engineering* notebook of day 1 once more. Unfortunately, this will take a couple of minutes." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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322015-03-22T00:00:00.000+000015282.0793.01036.0571.0411.0258.02014.02014.0978.0258.0618.02014.0978.0103.879321181620387.0562698996761455459.060186488301856.4609593226101141.227382567328235.668634830464441171.2407305689485225.044911937768430000
322015-03-22T01:00:00.000+000015283.0794.01037.0572.0412.0259.02015.02015.0979.0259.0619.02015.0979.0101.927663340969895.813729359231398455.6181237113162356.9583886107934242.255628131728555.131612356411994177.086612232956517.2640144410050060000
322015-03-22T02:00:00.000+000015284.0795.01038.0573.0413.0260.02016.02016.0980.0260.0620.02016.0980.0103.24425944470397.781789225930997472.571807762072830.89945609597592543.492985764343373.1464827813147282172.188937364868767.856848275297060000
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322015-04-05T10:00:00.000+000015628.0195.01382.03.0757.0604.02360.02360.01324.0604.0964.02360.01324.0102.955009928982453.209090272140571506.749509785870570.1589683717221841.610107906665153.7170729264843563170.461693304486236.9006470916774060000
322015-04-05T11:00:00.000+000015629.0196.01383.04.0758.0605.02361.02361.01325.0605.0965.02361.01325.098.740072074936898.421290934369408493.642556307705858.4089151059959940.771442309824653.9918413194435605178.0742932207627417.5949776278407020000
322015-04-05T12:00:00.000+000015630.0197.01384.05.0759.0606.02362.02362.01326.0606.0966.02362.01326.099.48505793845099.444891036317408509.157797972964542.9932293771912238.465591071988432.019227099788039180.7344009430432518.463305592895530000
322015-04-05T13:00:00.000+000015631.0198.01385.06.0760.0607.02363.02363.01327.0607.0967.02363.01327.0101.6138576042711910.29030304414476475.1584281936552539.1721464730374838.208877404898421.5526998343654226187.5764445408107713.502902751077940000
322015-04-05T14:00:00.000+000015632.0199.01386.07.0761.0608.02364.02364.01328.0608.0968.02364.01328.0105.898199968122213.502548397361313469.7762547887402343.0262415599361238.218754966978651.5431867479781718184.8996987217335318.4161681451886250000
322015-04-05T15:00:00.000+000015633.0200.01387.08.0762.0609.02365.02365.01329.0609.0969.02365.01329.0111.696940189463254.996833774513497454.218892338296729.185426909690339.682156296497653.5077022950782535167.9344660245457426.4350653354873550000
322015-04-05T16:00:00.000+000015634.0201.01388.09.0763.0610.02366.02366.01330.0610.0970.02366.01330.0109.394306573748757.601029147174564442.8073581320655311.93177268164257639.244129537051734.034858943479992162.7342419940222322.648196557989170000
322015-04-05T17:00:00.000+000015635.0202.01389.010.0764.0611.02367.02367.01331.0611.0971.02367.01331.0110.046620219771757.524004277715391460.1749535245014625.40911969043838838.94135046710673.9784602987326383157.4202103433052615.8613339345461740000
322015-04-05T18:00:00.000+000015636.0203.01390.011.0765.0612.02368.02368.01332.0612.0972.02368.01332.0103.83711829386827.89611664843841493.65168409872357.39620060834976638.247896591858334.5965344535872354154.9228093224642516.327362117800720000
322015-04-05T19:00:00.000+000015637.0204.01391.012.0766.0613.02369.02369.01333.0613.0973.02369.01333.0103.272319259306947.349300739667003495.3827290113912355.9264769392043737.6463182031649743.5601229828876244166.7817615856747613.580693796886020000
322015-04-05T20:00:00.000+000015638.0205.01392.013.0767.0614.02370.02370.01334.0614.0974.02370.01334.0102.388834573325977.964688646442502475.8204247879250584.1306851002626839.137478924602123.4313081552395794171.8442537507702617.5486085777895830000
322015-04-05T21:00:00.000+000015639.0206.01393.014.0768.0615.02371.02371.01335.0615.0975.02371.01335.0101.255448401555236.670592053947905451.6920644697129389.6547898247440138.864740958505223.4838601777209255174.2459909835917.4848172413667240000
322015-04-05T22:00:00.000+000015640.0207.01394.015.0769.0616.02372.02372.01336.0616.0976.02372.01336.0103.821046968022274.732184933006682426.722259415556549.18088859350941539.8591554111933562.0244708184819524177.7790395520962410.8203802405857040000
322015-04-05T23:00:00.000+000015641.0208.01395.016.0770.0617.02373.02373.01337.0617.0977.02373.01337.0102.737546941619773.889767817232084422.3757102290657645.6794092174772836.296985927588575.8642272069287715173.635990930352511.8071848744802460000
322015-04-06T00:00:00.000+000015642.0209.01396.017.0771.0618.02374.02374.01338.0618.0978.02374.01338.0103.103125245906943.1833022993201423452.6135453573159639.7667974761521934.0285192282139955.062564160450547173.290429221216511.2298470582808820000
322015-04-06T01:00:00.000+000015643.0210.01397.018.0772.0619.02375.02375.01339.0619.0979.02375.01339.0101.808422041680962.729845779459619468.411499006151519.68761607962743634.569834146202675.698328184445998168.70403598519212.6003574774655930000
322015-04-06T02:00:00.000+000015644.0211.01398.019.0773.0620.02376.02376.01340.0620.0980.02376.01340.0105.145533608033458.68356823912892466.6433285790959619.37449310355979634.672851680920735.7916683012270695168.9394368117900212.4677769708231450000
322015-04-06T03:00:00.000+000015645.0212.01399.020.0774.0621.02377.02377.01341.0621.0981.02377.01341.0104.919572291684468.747807328608319460.336901518414530.1103747067898437.95445562454084.020995116522143172.5261022057994813.1549526968784780000
322015-04-06T04:00:00.000+000015646.0213.01400.021.0775.0622.02378.02378.01342.0622.0982.02378.01342.0104.90320965624558.763799773917269467.80103619515341.8789521583405240.693646427706351.7961811737464364160.31070857870317.0807275138712350000
322015-04-06T05:00:00.000+000015647.0214.01401.022.0776.0623.02379.02379.01343.0623.0983.02379.01343.097.7599318772257819.027913554694774454.071815697381851.5301610267837339.783505772637032.815842616565917162.45492194808717.4220662489973180000
322015-04-06T06:00:00.000+000015648.0215.01402.023.0777.0624.02380.02380.01344.0624.0984.02380.01344.094.8149058105170215.474888207570832459.902409641032554.643396499813140.7136721120084962.9631117411381815163.8326323109002417.798594895958330000
322015-04-06T07:00:00.000+000015649.0216.01403.024.0778.0625.02381.02381.01345.0625.0985.02381.01345.094.5541859416937715.305111590280386467.1504205459972449.1020656870065939.21587007025394.1284341283999915164.3945447736132618.4905273665261980000
322015-04-06T08:00:00.000+000015650.0217.01404.025.0779.0626.02382.02382.01346.0626.0986.02382.01346.097.4590425740205117.2701394450736464.89668487857145.87289357062406538.709242822668573.5940071073666826173.3653857628726.3725082275376180000
322015-04-06T09:00:00.000+000015651.0218.01405.026.0780.0627.02383.02383.01347.0627.0987.02383.01347.0105.56089511208053.5052275198402816468.34172141850340.4591841254066737.8589454314295254.567506423955181170.2970757737932411.1520814013428250000
322015-04-06T10:00:00.000+000015652.0219.01406.027.0781.0628.02384.02384.01348.0628.0988.02384.01348.0104.60957741630153.8437937831937967476.229763242537548.3679818220419936.431618057609023.3954293125915544165.984689156693514.4159513973367770000
322015-04-06T11:00:00.000+000015653.0220.01407.028.0782.0629.02385.02385.01349.0629.0989.02385.01349.0105.655721150786763.313313580520711479.335172970131546.33109702581961636.6475676867265043.2914370505354604162.9735723655252610.7486062460998880000
322015-04-06T12:00:00.000+000015654.0221.01408.029.0783.0630.02386.02386.01350.0630.0990.02386.01350.0101.548557553640535.47710509651552456.949908732254846.56668640781436637.0033301405366763.933559309752731160.843185816050248.3519642278083080000
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df = spark.read.parquet(\"dbfs:/FileStore/tables/preprocessed\").cache()\n", "df = df.withColumnRenamed(\"y_0\", \"label\") # this is the label we're going to use\n", "\n", "display(df)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "keys = ['machineID', 'datetime']\n", "X_keep = ['diff_maint_1', 'diff_error_1', 'volt_sd_3', 'diff_fail_3', 'pressure_ma_3', 'pressure_sd_3', 'diff_fail_1', 'diff_fail_0', 'age', 'vibration_ma_3', 'rotate_ma_3', 'diff_error_2', 'diff_fail_2', 'diff_error_3', 'diff_maint_2', 'volt_ma_3', 'diff_maint_0', 'vibration_sd_3', 'diff_maint_3', 'rotate_sd_3', 'diff_error_0', 'diff_error_4']\n", "Y_keep = ['y_0', 'y_1', 'y_2', 'y_3']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's begin by dividing the data into training and test sets. With time-series data, we usually divide the data based on a time cut-off and to avoid **leakage** we also put a gap (2 weeks in this case) between the training and test data. Another option we have is to sample every n-th row of the data. The data is collected hourly, and if we do not wish to use such a high frequency for modeling, we can sample every n-th row of the data." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# from pyspark.sql.types import DateType\n", "from pandas import datetime\n", "from pyspark.sql.functions import col, hour\n", "\n", "# we sample every nth row of the data using the `hour` function\n", "df_train = df.filter((col('datetime') < datetime(2015, 10, 1))) # & (hour(col('datetime')) % 3 == 0))\n", "df_test = df.filter(col('datetime') > datetime(2015, 10, 15))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's look at some summary statistics for the labels in the data." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
summarymachineIDagediff_error_0diff_error_1diff_error_2diff_error_3diff_error_4diff_fail_0diff_fail_1diff_fail_2diff_fail_3diff_maint_0diff_maint_1diff_maint_2diff_maint_3pressure_ma_3pressure_sd_3rotate_ma_3rotate_sd_3vibration_ma_3vibration_sd_3volt_ma_3volt_sd_3labely_1y_2y_3
count654600654600654600654600654600654600654600654600654600654600654600654600654600654600654600654600654600654600654600654600654600654600654600654600654600654600654600
mean50.511.33767.0613458600673998.2097525206233933.5928353192791020.79924075771461615.12518942865882133.1543996333641935.93174610449142652.42126947754332349.70948212648951113.88324472960581109.7713840513291144.65145737855191120.9993217231897100.825134520184389.23999324497866446.671080080802446.20725438107571440.3964837222775944.619880429306799170.7915328226606813.815335825063860.0146379468377635190.018849679193400550.0108203483043079750.015073327222731438
stddev28.8660920963801575.8276197440095325763.1257842489293955.9490918887524994.1383169517087995.11941541641161427.18205098762971655.12930839179171552.7992166091151935.5717588200811866.57333153854941000.67492911812771023.52386226274111038.59935908860621038.3281999294046.783110559195193.91649947352247429.65933821520036819.4865553158303163.17229818138536231.95330447988257478.4480169118941655.8454271175128820.120098706839455670.135994106639518970.103456680392130280.1218447565916889
min100.00.00.00.00.00.00.00.00.00.00.00.00.076.00533246775320.09369705911616336198.7703373777620.2159233438022444622.97328948186140.016413819956125182135.97945655893550.02999196817971350000
max100205482.06645.06645.06617.06645.06645.06645.06645.06645.06645.06645.06645.06645.0164.8753240447777632.69926439338247576.923563024815164.9177646109864467.3120852001053715.995336982084046233.1357763038757654.5842292504167261111
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "display(df_train.describe())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's make sure we don't have any null values in our DataFrame." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "
We have 0 records that contain null values.\n", "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "recordCount = df_train.count()\n", "noNullsRecordCount = df_train.na.drop().count()\n", "\n", "print(\"We have {} records that contain null values.\".format(recordCount - noNullsRecordCount))" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
labelcount
19582
0645018
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "display(df_train.groupBy(\"label\").count())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Train a Logistic Regression Model\n", "\n", "Let's now start training our logistic regression model. It's always a good ideas to double-check schemas." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "
root\n", "-- machineID: integer (nullable = true)\n", "-- datetime: timestamp (nullable = true)\n", "-- age: integer (nullable = true)\n", "-- diff_error_0: double (nullable = true)\n", "-- diff_error_1: double (nullable = true)\n", "-- diff_error_2: double (nullable = true)\n", "-- diff_error_3: double (nullable = true)\n", "-- diff_error_4: double (nullable = true)\n", "-- diff_fail_0: double (nullable = true)\n", "-- diff_fail_1: double (nullable = true)\n", "-- diff_fail_2: double (nullable = true)\n", "-- diff_fail_3: double (nullable = true)\n", "-- diff_maint_0: double (nullable = true)\n", "-- diff_maint_1: double (nullable = true)\n", "-- diff_maint_2: double (nullable = true)\n", "-- diff_maint_3: double (nullable = true)\n", "-- pressure_ma_3: double (nullable = true)\n", "-- pressure_sd_3: double (nullable = true)\n", "-- rotate_ma_3: double (nullable = true)\n", "-- rotate_sd_3: double (nullable = true)\n", "-- vibration_ma_3: double (nullable = true)\n", "-- vibration_sd_3: double (nullable = true)\n", "-- volt_ma_3: double (nullable = true)\n", "-- volt_sd_3: double (nullable = true)\n", "-- label: integer (nullable = true)\n", "-- y_1: integer (nullable = true)\n", "-- y_2: integer (nullable = true)\n", "-- y_3: integer (nullable = true)\n", "\n", "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df_train.printSchema()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Setting up the model\n", "\n", "We set the `label` column of the LogisticRegression model to `error`, and the `features` column to `norm_features`." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from pyspark.ml.feature import StandardScaler, VectorAssembler\n", "from pyspark.ml.classification import LogisticRegression\n", "from pyspark.ml import Pipeline\n", "\n", "vassembler = VectorAssembler(inputCols = X_keep, outputCol = \"features\")\n", "stndscaler = StandardScaler(inputCol = \"features\", outputCol = \"norm_features\")\n", "\n", "lr = (LogisticRegression()\n", " .setLabelCol(\"label\")\n", " .setFeaturesCol(\"norm_features\"))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Hands-on lab\n", "Create a pipeline that contains a single stage for the model we created above. Then fit the pipeline to the training data and then use the fitted model to `transform` the test data. Finally, return the counts of TP, TN, FP, and FN." ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# put your solution here\n", "\n", "# pipeline = Pipeline(stages = [])\n", "# assert len(pipeline.getStages()) == 3 # make sure it's one stage only\n", "\n", "# fit the pipeline\n", "# get predictions on the test data\n", "# group data by predictions and labels to get counts for TP, TN, FP, FN" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "
[VectorAssembler_e29af6a2453e, StandardScaler_5cb04cfc4ad2, LogisticRegression_9d0a1cef5ab5]\n", "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# maximize this cell to see the solution:\n", "\n", "pipeline = Pipeline(stages = [vassembler, stndscaler, lr])\n", "\n", "assert len(pipeline.getStages()) == 3 # make sure it's one stage only\n", "print(pipeline.getStages())\n", "\n", "lr_model = pipeline.fit(df_train)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can now run our pipeline on the test data to obtain predictions." ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
labelpredictioncount
10.02004
00.0185558
11.084
01.0154
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# maximize this cell to see the solution:\n", "\n", "df_pred = lr_model.transform(df_test).drop(\"features\", \"norm_features\") # apply the model to our held-out test set\n", "display(df_pred.groupBy(\"label\", \"prediction\").count())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### End of lab" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "
root\n", "-- machineID: integer (nullable = true)\n", "-- datetime: timestamp (nullable = true)\n", "-- age: integer (nullable = true)\n", "-- diff_error_0: double (nullable = true)\n", "-- diff_error_1: double (nullable = true)\n", "-- diff_error_2: double (nullable = true)\n", "-- diff_error_3: double (nullable = true)\n", "-- diff_error_4: double (nullable = true)\n", "-- diff_fail_0: double (nullable = true)\n", "-- diff_fail_1: double (nullable = true)\n", "-- diff_fail_2: double (nullable = true)\n", "-- diff_fail_3: double (nullable = true)\n", "-- diff_maint_0: double (nullable = true)\n", "-- diff_maint_1: double (nullable = true)\n", "-- diff_maint_2: double (nullable = true)\n", "-- diff_maint_3: double (nullable = true)\n", "-- pressure_ma_3: double (nullable = true)\n", "-- pressure_sd_3: double (nullable = true)\n", "-- rotate_ma_3: double (nullable = true)\n", "-- rotate_sd_3: double (nullable = true)\n", "-- vibration_ma_3: double (nullable = true)\n", "-- vibration_sd_3: double (nullable = true)\n", "-- volt_ma_3: double (nullable = true)\n", "-- volt_sd_3: double (nullable = true)\n", "-- label: integer (nullable = true)\n", "-- y_1: integer (nullable = true)\n", "-- y_2: integer (nullable = true)\n", "-- y_3: integer (nullable = true)\n", "-- rawPrediction: vector (nullable = true)\n", "-- probability: vector (nullable = true)\n", "-- prediction: double (nullable = false)\n", "\n", "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df_pred.printSchema()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Evaluate the Model" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "
labelCol: label column name. (default: label)\n", "metricName: metric name in evaluation (areaUnderROC|areaUnderPR) (default: areaUnderROC)\n", "rawPredictionCol: raw prediction (a.k.a. confidence) column name. (default: rawPrediction)\n", "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from pyspark.ml.evaluation import BinaryClassificationEvaluator\n", "\n", "evaluator = BinaryClassificationEvaluator()\n", "print(evaluator.explainParams())" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "
areaUnderROC: 0.8184575467984492\n", "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "evaluator.setLabelCol(\"label\")\n", "evaluator.setRawPredictionCol('rawPrediction')\n", "\n", "metricName = evaluator.getMetricName()\n", "metricVal = evaluator.evaluate(df_pred)\n", "\n", "print(\"{}: {}\".format(metricName, metricVal))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We could wrap this into a function to make it easier to get the output of multiple metrics." ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "
AUROC: 0.8184575467984502\n", "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "evaluator = BinaryClassificationEvaluator()\n", "evaluator.setLabelCol(\"label\")\n", "evaluator.setRawPredictionCol(\"rawPrediction\")\n", "\n", "auroc = evaluator.setMetricName(\"areaUnderROC\").evaluate(df_pred)\n", "\n", "print(\"AUROC: {}\".format(auroc))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's now save the pipeline in case we need to make future use of it." ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "lr_model.write().overwrite().save(\"/dbfs/tmp/lr_model_ipeline\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Conclusion\n", "Hmmmm... our results are not great yet. We'll look into how to improve our results later." ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# You can ignore this code, we use it for testing our notebooks.\n", "assert auroc > .8" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Copyright (c) Microsoft Corporation. All rights reserved.\n", "\n", "Licensed under the MIT License." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3.5", "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": "3.5.5" }, "name": "01_logistic_regression", "notebookId": 4057188818416322 }, "nbformat": 4, "nbformat_minor": 1 }