Ajay Borra ajayborra
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| //linear regression model | |
| val lr = new LinearRegression().setLabelCol("medianHouseValue").setFeaturesCol("scaledFeatures") | |
| .setMaxIter(100) | |
| .setRegParam(0.3) | |
| .setElasticNetParam(0.8) | |
| // Using Training set for model building | |
| val lrModel = lr.fit(split(0)) | |
| // Print the coefficients and intercept for linear regression | |
| println(s"Coefficients: ${lrModel.coefficients} Intercept: ${lrModel.intercept}") |
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| //divide in test and train | |
| val split = scaledFeatures.randomSplit(Array(.8, .2)) |
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| +----------------+--------------------+--------------------+ | |
| |medianHouseValue| features| scaledFeatures| | |
| +----------------+--------------------+--------------------+ | |
| | 452600.0|[8.3252,41.0,880....|[2.34470895611761...| | |
| | 358500.0|[8.3014,21.0,7099...|[2.33218146484030...| | |
| | 352100.0|[7.2574,52.0,1467...|[1.78265621721384...| | |
| | 341300.0|[5.6431,52.0,1274...|[0.93294490759373...| | |
| | 342200.0|[3.8462,52.0,1627...|[-0.0128806838430...| | |
| | 269700.0|[4.0368,52.0,919....|[0.08744451941137...| | |
| | 299200.0|[3.6591,52.0,2535...|[-0.1113636089684...| |
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| //Using Standard scaler to scale the feature set | |
| val standardScaler = new StandardScaler() | |
| .setInputCol("features") | |
| .setOutputCol("scaledFeatures") | |
| .setWithStd(true) | |
| .setWithMean(true) | |
| val scaler = standardScaler.fit(featuresDf) | |
| val scaledFeatures = scaler.transform(featuresDf) | |
| //print the output | |
| scaledFeatures.show() |
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| // create features | |
| val featureCols = Array("medianIncome", "housingMedianAge", "totalRooms", "totalBedrooms", | |
| "population", "households", "latitude", "longitude") | |
| val vectorAssembler = new VectorAssembler().setInputCols(featureCols).setOutputCol("features") | |
| //create Label and Features | |
| val featuresDf = vectorAssembler.transform(castedDF).select("medianHouseValue", "features") | |
| //print label/feature | |
| featuresDf.show() |
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| // cast all the strings to Double type in Data Frame | |
| val castedDF = df.columns.foldLeft(df)((current, c) => | |
| current.withColumn(c, current(c).cast(DoubleType))) | |
| castedDF.describe().show() |
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| //read the data as data frame | |
| val df = spark.read.format("csv").option("header", "true") | |
| .load("/path/to/file.csv") |
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| // set up spark context | |
| implicit val spark = SparkSession.builder.master("local") | |
| .appName("California Housing Dataset Prediction").getOrCreate |
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| // Displays the content of the DataFrame to stdout | |
| df.show() | |
| Output: | |
| +-------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+ | |
| |houseId| medianHouseValue| medianIncome| housingMedianAge| totalRooms| totalBedrooms| population| households| latitude| longitude| | |
| +-------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+ | |
| | 1|4.526000000000000...|8.325200000000000...|4.100000000000000...|8.800000000000000...|1.290000000000000...|3.220000000000000...|1.260000000000000...|3.788000000000000...|-1.22230000000000...| | |
| | 2|3.585000000000000...|8.301399999999999...|2.100000000000000...|7.099000000000000...|1.106000000000000...|2.401000000000000...|1.138000000000000...|3.7 |
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