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Copy pathecommerce_analysis_fullcode.scala
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ecommerce_analysis_fullcode.scala
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val eRdd = sc.textFile("ecom_data.csv")
eRdd.count
// To remove 1st header row
val eRdd1 = eRdd.mapPartitionsWithIndex {
(idx, iter) => if (idx == 0) iter.drop(1) else iter
}
val ePairRdd = eRdd1.map{ l =>
val str0 = l.split(',')
val (oid,cid,qt,cp,sp) = (str0(0),str0(1),str0(2).toInt,str0(3).toFloat,str0(4).toFloat)
val (ts,rating,pCat,pId,sp_type) = (str0(5),str0(6).toInt,str0(7),str0(8),str0(9))
val (oStat,pWt,pLen,pHt,pWidth) = (str0(10),str0(11).toInt,str0(12).toInt,str0(13).toInt,str0(14).toInt)
val (cCity,cState,sId,sCity,sState) = (str0(15),str0(16),str0(17),str0(18),str0(19))
val (sInstal) = (str0(20).toInt)
(oid,cid,qt,cp,sp,ts,rating,pCat,pId,sp_type,
oStat,pWt,pLen,pHt,pWidth,cCity,
cState,sId,sCity,sState,sInstal) }
ePairRdd.toDF("oid","cid","qt","cp","sp","ts","rating","pCat","pId","sp_type",
"oStat","pWt","pLen","pHt","pWidth","cCity",
"cState","sId","sCity","sState","sInstal").show
/*
+--------------------+--------------------+---+-----+------+----------------+------+-------------+--------------------+-----------+---------+---+----+---+------+--------------------+------+--------------------+--------------------+------+-------+
| oid| cid| qt| cp| sp| ts|rating| pCat| pId| sp_type| oStat|pWt|pLen|pHt|pWidth| cCity|cState| sId| sCity|sState|sInstal|
+--------------------+--------------------+---+-----+------+----------------+------+-------------+--------------------+-----------+---------+---+----+---+------+--------------------+------+--------------------+--------------------+------+-------+
|d1ff908b4e21d4fff...|190508c583e9da289...| 1|207.9|238.61|04-08-2018 21:57| 5|health_beauty|08462528607b71ea6...|credit_card|delivered|650| 16| 10| 11|sao goncalo do am...| RN|ccc4bbb5f32a6ab2b...| curitiba| PR| 8|
|093c11a6b12993f96...|77a6136f49bac0c33...| 1|364.0|797.38|13-06-2018 08:52| 5|health_beauty|6cdd53843498f9289...|credit_card|delivered|900| 25| 12| 38|jaboatao dos guar...| PE|ccc4bbb5f32a6ab2b...| curitiba| PR| 6|
|093c11a6b12993f96...|77a6136f49bac0c33...| 2|364.0|797.38|13-06-2018 08:52| 5|health_beauty|6cdd53843498f9289...|credit_card|delivered|900| 25| 12| 38|jaboatao dos guar...| PE|ccc4bbb5f32a6ab2b...| curitiba| PR| 6|
|d08bba6d656adbdc5...|2bbb7f863b68ba0f0...| 2|364.0|479.22|13-07-2018 20:40| 1|health_beauty|6cdd53843498f9289...|credit_card|delivered|900| 25| 12| 38| rio de janeiro| RJ|ccc4bbb5f32a6ab2b...| curitiba| PR| 2|
|8be26417e499e19c5...|08345c45f60f73773...| 1|364.0| 428.8|05-08-2018 23:13| 5|health_beauty|6cdd53843498f9289...|credit_card|delivered|900| 25| 12| 38| toritama| PE|ccc4bbb5f32a6ab2b...| curitiba| PR| 8|
|0d711a2c41f1b9365...|213904d01203d2e28...| 1|349.9|365.43|24-04-2018 15:27| 5|health_beauty|6cdd53843498f9289...|credit_card|delivered|900| 25| 12| 38| rio de janeiro| RJ|ccc4bbb5f32a6ab2b...| curitiba| PR| 1|
|d7bcd768f38943877...|bce98ac8bc5881286...| 1|349.9|398.46|08-05-2018 09:22| 5|health_beauty|6cdd53843498f9289...|credit_card|delivered|900| 25| 12| 38| anadia| AL|ccc4bbb5f32a6ab2b...| curitiba| PR| 8|
|91d9b7669f0960199...|c9341f47a1c2979e0...| 1|349.9|370.52|10-04-2018 14:36| 5|health_beauty|6cdd53843498f9289...|credit_card|delivered|900| 25| 12| 38| rio de janeiro| RJ|ccc4bbb5f32a6ab2b...| curitiba| PR| 8|
|48af7589aa4fbcb01...|e2f2b94effb9d9eab...| 1|364.0| 395.8|04-08-2018 17:09| 5|health_beauty|6cdd53843498f9289...|credit_card|delivered|900| 25| 12| 38| aracaju| SE|ccc4bbb5f32a6ab2b...| curitiba| PR| 6|
|209b30577b4c8a3f6...|f3cbdf0bb95fae3bb...| 1|349.9|381.86|04-04-2018 20:50| 5|health_beauty|6cdd53843498f9289...|credit_card|delivered|900| 25| 12| 38| aracaju| SE|ccc4bbb5f32a6ab2b...| curitiba| PR| 5|
|5262eaeb971616ffe...|35f335e770081fe4f...| 1| 28.9| 0.67|08-06-2018 18:51| 5|health_beauty|d1a57f4402dbfe2cb...|credit_card|delivered|650| 22| 12| 22| linhares| ES|2ff97219cb8622eaf...| salto| SP| 1|
|5262eaeb971616ffe...|35f335e770081fe4f...| 1| 28.9| 47.55|08-06-2018 18:51| 5|health_beauty|d1a57f4402dbfe2cb...| voucher|delivered|650| 22| 12| 22| linhares| ES|2ff97219cb8622eaf...| salto| SP| 1|
|946f3e7b0d6366f44...|0e6e8c5b4485a66e6...| 1| 31.9| 97.31|09-08-2018 07:25| 3|health_beauty|d1a57f4402dbfe2cb...|credit_card|delivered|650| 22| 12| 22| niteroi| RJ|2ff97219cb8622eaf...| salto| SP| 8|
|1b0e1058a094f3339...|91e07c844b9c9cd8a...| 1| 29.9| 39.42|09-07-2018 19:51| 5|health_beauty|d1a57f4402dbfe2cb...|credit_card|delivered|650| 22| 12| 22| hortolandia| SP|2ff97219cb8622eaf...| salto| SP| 1|
|a684a880595e155b0...|6a43e91f4891c9b7b...| 1| 49.8| 68.03|10-06-2018 18:51| 1|health_beauty|8ddf5554f2617f632...|credit_card|delivered|400| 16| 2| 11| ribeirao preto| SP|2a1348e9addc1af5a...| belo horizonte| MG| 2|
|9d65f5b3f39a8bab1...|d73568e3ecaf64187...| 1| 84.9| 93.79|24-05-2018 23:50| 5|health_beauty|c46c87888b081cf42...|credit_card|delivered|450| 27| 11| 19| cajamar| SP|41e0fa5761c886a63...| guaruja| SP| 8|
|d0d2bc60ab96a5450...|23e8f9579ef5c6aef...| 1| 77.0| 90.88|30-06-2018 13:32| 5|health_beauty|719d571299707561c...|credit_card|delivered|434| 20| 16| 18| imbau| PR|b90e891671cffd955...|sao jose dos pinhais| PR| 4|
|48e70325bb404002e...|95556cea2fc7ddfac...| 1|199.0|427.02|05-05-2018 08:28| 1|health_beauty|a6f2ba5bb6d6d36ac...|credit_card|delivered|450| 20| 15| 15| sertanopolis| PR|b90e891671cffd955...|sao jose dos pinhais| PR| 3|
|48e70325bb404002e...|95556cea2fc7ddfac...| 2|199.0|427.02|05-05-2018 08:28| 1|health_beauty|a6f2ba5bb6d6d36ac...|credit_card|delivered|450| 20| 15| 15| sertanopolis| PR|b90e891671cffd955...|sao jose dos pinhais| PR| 3|
|717b5830932c155c6...|acec81168c11c0d01...| 1|140.0|159.08|23-07-2018 12:50| 5|health_beauty|d4e19eb1e5c5d6194...|credit_card|delivered|300| 16| 20| 15| mogi-guacu| SP|b90e891671cffd955...|sao jose dos pinhais| PR| 5|
+--------------------+--------------------+---+-----+------+----------------+------+-------------+--------------------+-----------+---------+---+----+---+------+--------------------+------+--------------------+--------------------+------+-------+
*/
// Customer Segmentation:
// ----------------------
val ePairRdd2 = ePairRdd.map{ case(oid,cid,qt,cp,sp,ts,rating,pCat,pId,sp_type,
oStat,pWt,pLen,pHt,pWidth,cCity,
cState,sId,sCity,sState,sInstal) => (cid,qt,sp) }
val ePairRdd4 = ePairRdd2.map{ case(cid,qt,sp) =>
val amount = qt*sp
val amount_roundoff = (math rint amount*100)/100
(cid,amount_roundoff) }.reduceByKey(_+_).sortBy(_._2,false)
ePairRdd4.toDF("Customer ID","Bill Amount").show
/*
+--------------------+------------------+
| Customer| Bill Amount|
+--------------------+------------------+
|daf15f1b940cc6a72...| 97263.66|
|ec5b2ba62e5743423...| 72748.8|
|f7622098214b4634b...| 68099.64|
|71901689c5f3e5adc...| 67110.33|
|159132ab31eb3c8a1...| 45873.8|
|7d321bd4e8ba1caf7...| 44220.54|
|d95ca02ab50105ccc...| 35690.16|
|6152fbfc8a92ee25f...|31227.199999999997|
|222e4c4d91c814caf...| 29489.04|
|dd3f1762eb601f41c...| 28380.24|
|0179f2f4c32e0b0c2...| 27471.78|
|5c9d09439a7815d2c...| 26341.5|
|ce90096f130e38392...| 22130.64|
|0d554604c3b40dee6...|21498.120000000003|
|4a16dfed3e9e57741...| 21122.64|
|4dc417fbc348bf334...| 19339.74|
|9eb3d566e87289dcb...| 19033.56|
|94dd8366ec790734b...| 17362.8|
|a67a246af6ba598a1...|16322.320000000002|
|c0d62b7f7cbf132ac...| 16196.25|
+--------------------+------------------+
*/
val all_customers = ePairRdd4.map{ case (cid,amount) =>
if (amount>=50000) {( "VIP customers", List((cid,amount)) )}
else if ((amount>=20000)&&(amount<50000)) {( "Silver customers", List((cid,amount)) )}
else if ((amount>=5000) &&(amount<20000)) {( "Bronze customers", List((cid,amount)) )}
else {( "Lowtime customers", List((cid,amount)) )}
}.reduceByKey(_ ++ _)
all_customers.toDF("Customer Type","Customer_id & Bill amount").show
/*
+-----------------+-------------------------+
| Customer Type|Customer_id & Bill amount|
+-----------------+-------------------------+
|Lowtime customers| [[a7c105e27ad87e9...|
| VIP customers| [[daf15f1b940cc6a...|
| Silver customers| [[159132ab31eb3c8...|
| Bronze customers| [[4dc417fbc348bf3...|
+-----------------+-------------------------+
*/
val all_customers_count = all_customers.map { case (customer_type,list)=> (customer_type, list.length) }.sortBy(_._2)
all_customers_count.toDF("Customer Type","Customer count").show
/*
+-----------------+--------------+
| Customer Type|Customer count|
+-----------------+--------------+
| VIP customers| 4|
| Silver customers| 11|
| Bronze customers| 50|
|Lowtime customers| 9475|
+-----------------+--------------+
*/
val file = new java.io.PrintStream("BDA_Project\\customer_type.csv")
all_customers_count.collect.foreach ( file.println(_) )
file.close
// Monthly Trend Forecasting:
// -------------------------
val ePairRdd5 = ePairRdd.map{ case(oid,cid,qt,cp,sp,ts,rating,pCat,pId,sp_type,oStat,pWt,pLen,pHt,pWidth,cCity,cState,sId,sCity,sState,sInstal) =>
val ds_ts = ts.split(" ")
val month = ds_ts(0).split("-")
(pId,qt,month(1).toInt) }.sortBy(_._3)
val all_months = ePairRdd5.map{ case (pId,qt,month) =>
( month, List((pId,qt)) ) }.reduceByKey(_ ++ _).sortBy(_._1)
val all_months_count = ePairRdd5.map{ case (pId,qt,month) =>
( month,qt) }.reduceByKey(_+_).sortBy(_._1)
val ePairRdd6 = ePairRdd.map{ case(oid,cid,qt,cp,sp,ts,rating,pCat,pId,sp_type,oStat,pWt,pLen,pHt,pWidth,cCity,cState,sId,sCity,sState,sInstal) =>
val ds_ts = ts.split(" ")
val month = ds_ts(0).split("-")
(month(1).toInt,cid) }.reduceByKey(_++_).sortBy(_._1)
val monthly_customers_count = ePairRdd6.map { case (month,cid)=> (month,cid.length) }.sortBy(_._1)
val monthly_price = ePairRdd.map{ case(oid,cid,qt,cp,sp,ts,rating,pCat,pId,sp_type,oStat,pWt,pLen,pHt,pWidth,cCity,cState,sId,sCity,sState,sInstal) =>
val ds_ts = ts.split(" ")
val month = ds_ts(0).split("-")
(month(1).toInt,sp) }.reduceByKey(_+_).sortBy(_._1)
// Joining 3 RDDs
val monthly_analysis_joint = (all_months_count.join(monthly_customers_count)).join(monthly_price).sortBy(_._1)
monthly_analysis_joint.toDF.show
val monthly_analysis = monthly_analysis_joint.map{ case(month,q_c_p)=>
val price = q_c_p._2
val q_c = q_c_p._1
val qt = q_c._1
val customer_count = q_c._2
(month,qt,customer_count,price) }
monthly_analysis.toDF("Month","Quantity","Customer count","Price").show
/*
+-----+--------+--------------+---------+
|Month|Quantity|Customer count| Price|
+-----+--------+--------------+---------+
| 1| 5| 160|705.42004|
| 2| 29| 608| 6474.93|
| 3| 121| 3616|25786.564|
| 4| 2353| 56480| 354047.6|
| 5| 3478| 87616| 579959.3|
| 6| 3145| 79104|492078.03|
| 7| 2901| 75936| 474321.5|
| 8| 2654| 71584|387912.56|
| 9| 1| 32| 317.85|
| 10| 3| 96|285.03998|
| 11| 1| 32| 133.76|
| 12| 4| 128| 197.36|
+-----+--------+--------------+---------+
*/
val file = new java.io.PrintStream("BDA_Project\\monthly_analysis.csv")
monthly_analysis.collect.foreach ( file.println(_) )
file.close
// Monthly Profit Analysis:
// ------------------------
val pRdd=ePairRdd.map{case(oid,cid,qt,cp,sp,ts,rating,pCat,pId,sp_type,oStat,pWt,pLen,pHt,pWidth,cCity,cState,sId,sCity,sState,sInstal)=>
val t1=ts.split(" ")(0).split("-")(1)
(t1,(List(sp-cp),List(sp)))}.reduceByKey{case(a,b)=>(a._1++b._1,a._2++b._2)}.map{case(a,(b,c))=>
val ps=b.sum
val ss=c.sum
val n=b.length
(a,ps,ps/n,ps*100.0/ss)}.sortBy(x=>x._1)
pRdd.toDF("Month","Net Profit","Avg Profit","Profit Percentage").show
/*
+-----+----------+----------+-------------------+
|Month|Net Profit|Avg Profit| Profit Percentage|
+-----+----------+----------+-------------------+
| 01| 89.72001| 17.944002| 12.718665621005606|
| 02| 3902.72| 205.40631| 60.274317479141494|
| 03| -838.5501|-7.4207973| -3.251887669594775|
| 04| 138351.0| 78.38583| 39.076966910684106|
| 05| 206943.22| 75.581894| 35.682460739331795|
| 06| 178368.52| 72.15555| 36.24804662730084|
| 07| 152446.88| 64.242256| 32.139984148411976|
| 08|109504.805| 48.951633| 28.229238020440647|
| 09| 17.860016| 17.860016| 5.619007558969206|
| 10| -163.96|-54.653336|-57.521758122693434|
| 11| 13.859993| 13.859993| 10.361837283305736|
| 12| 35.39| 8.8475| 17.931698054419353|
+-----+----------+----------+-------------------+
*/
val file=new java.io.PrintStream("BDA_Project\\Monthly_Profit.csv")
pRdd.collect.foreach{x=> file.println(x._1+","+x._2+","+x._3+","+x._4)}
file.close
// Hourly sales analysis:
// ----------------------
val TRdd=ePairRdd.map{case(oid,cid,qt,cp,sp,ts,rating,pCat,pId,sp_type,oStat,pWt,pLen,pHt,pWidth,cCity,cState,sId,sCity,sState,sInstal)=>
val t1=ts.split(" ")(1).split(":")(0)
(t1,(List(qt),List(sp)))}.reduceByKey{case(a,b)=>(a._1++b._1,a._2++b._2)}.map{case(a,(b,c))=>(a,b.sum,c.sum/c.length)}.sortBy(_._1)
TRdd.toDF("Hour","Quantity","Avg Price").show
/*
+----+--------+---------+
|Hour|Quantity|Avg Price|
+----+--------+---------+
| 00| 380|188.42192|
| 01| 157|135.26366|
| 02| 71|141.61308|
| 03| 46| 144.3241|
| 04| 55|141.85236|
| 05| 17| 83.02766|
| 06| 91|150.37776|
| 07| 185|217.24957|
| 08| 478|171.79517|
| 09| 778| 207.7129|
| 10| 908| 186.6949|
| 11| 943| 165.2758|
| 12| 835|198.04971|
| 13| 1003| 203.4392|
| 14| 1060|272.49692|
| 15| 842|195.80493|
| 16| 1161|199.62231|
| 17| 833|181.16876|
| 18| 926|221.36327|
| 19| 858|216.57292|
+----+--------+---------+
*/
val file = new java.io.PrintStream("BDA_Project\\Hourly_analysis.csv")
TRdd.sortBy(x=>x._1).collect.foreach{x=> file.println(x._1+","+x._2+","+x._3)}
file.close
// Product Wise Analysis:
// ----------------------
val prodRdd=ePairRdd.map{case(oid,cid,qt,cp,sp,ts,rating,pCat,pId,sp_type,oStat,pWt,pLen,pHt,pWidth,cCity,cState,sId,sCity,sState,sInstal)=>
(pId,(List(rating),List(sp)))}.reduceByKey{case(a,b)=>(a._1++b._1,a._2++b._2)}.map{case(a,(b,c))=>(a,b.sum*1.0/b.length,c.sum/c.length,c.length)}
// Total number of unique products sold:
prodRdd.count
//res28: Long = 6068
prodRdd.toDF("Product ID","Avg Rating","Avg Sales","No. of Orders").show
/*
+--------------------+-----------------+---------+-------------+
| Product ID| Avg Rating|Avg Sales|No. of Orders|
+--------------------+-----------------+---------+-------------+
|2196812e80b905c70...| 5.0| 141.97| 1|
|e24f73b7631ee3fbb...| 4.0| 136.6| 2|
|12fc9ab82dd45f382...| 4.5| 21.84| 2|
|486cebbfac430b06c...| 1.0| 232.88| 1|
|04c31b0da644f1782...| 5.0| 79.23| 1|
|9680a78df870a1cb6...| 4.0| 108.63| 1|
|f34152d311875e9da...| 5.0| 46.61| 1|
|b914a18d4547eff29...| 1.0| 767.33| 1|
|af407c297ba8ec158...| 5.0| 144.46| 1|
|192d363be840c4c30...| 5.0| 66.71| 1|
|b05fae603a3a28a97...| 4.0| 316.4| 1|
|31c79131e883e5fd8...| 5.0| 173.76| 2|
|ee08e2b1f2f38ce75...| 5.0| 51.06| 1|
|02a97df83a8a100c7...| 1.0| 78.72| 1|
|176faef125e3675b4...| 1.0| 44.27| 1|
|3ecdc0c6f18cf5c74...| 5.0| 149.01| 1|
|4ecb94d07e0dc5367...| 5.0| 264.33| 1|
|da1415c61b55b4e9e...|4.333333333333333| 161.27| 3|
|a6a8b6b05afd3a6e6...| 1.0| 109.24| 1|
|12803e6c487560862...| 5.0| 2223.12| 1|
+--------------------+-----------------+---------+-------------+
*/
val pSorted=prodRdd.sortBy( x => (x._4), ascending = true).collect.toList
// Top-10 Most Sold products:
pSorted.reverse.toDF("Product ID","Avg Rating","Avg Sales","No. of Orders").show
/*
+--------------------+------------------+---------+-------------+
| Product ID| Avg Rating|Avg Sales|No. of Orders|
+--------------------+------------------+---------+-------------+
|53b36df67ebb7c415...|3.9859154929577465|126.67379| 71|
|3fbc0ef745950c793...|3.3666666666666667| 111.7982| 60|
|19c91ef95d509ea33...| 4.033898305084746|156.36154| 59|
|422879e10f4668299...| 3.875|151.90225| 48|
|a92930c327948861c...|3.2954545454545454| 88.88952| 44|
|3dd2a17168ec895c7...|3.9069767441860463| 292.0474| 43|
|d1c427060a0f73f6b...|4.2439024390243905|173.98273| 41|
|a62e25e09e05e6faf...| 3.675| 300.078| 40|
|aca2eb7d00ea1a7b8...| 3.871794871794872|114.70897| 39|
|89b121bee266dcd25...|1.7222222222222223|100.56999| 36|
|d285360f29ac7fd97...|2.7142857142857144|241.91881| 35|
|e0d64dcfaa3b6db5c...| 2.6|152.95113| 35|
|368c6c730842d7801...| 4.735294117647059|140.83795| 34|
|99a4788cb24856965...| 4.121212121212121|141.62604| 33|
|4fe644d766c7566db...| 3.4375|111.36062| 32|
|fbce4c4cb307679d8...| 4.064516129032258|178.88127| 31|
|e7cc48a9daff5436f...|2.7666666666666666|283.18268| 30|
|389d119b48cf3043d...| 4.482758620689655|143.23035| 29|
|928e52a9ad53a294f...|2.8620689655172415|505.98178| 29|
|bb50f2e236e5eea01...| 4.24| 548.052| 25|
+--------------------+------------------+---------+-------------+
*/
val file = new java.io.PrintStream("BDA_Project\\Category_analysis.csv")
prodRdd.sortBy(_._4,false).collect.foreach ( x => file.println(x._1+","+x._2+","+x._3+","+x._4) )
file.close
// Least-10 sold products:
pSorted.toDF("Product ID","Avg Rating","Avg Sales","No. of Orders").show
/*
+--------------------+----------+---------+-------------+
| Product ID|Avg Rating|Avg Sales|No. of Orders|
+--------------------+----------+---------+-------------+
|2196812e80b905c70...| 5.0| 141.97| 1|
|486cebbfac430b06c...| 1.0| 232.88| 1|
|04c31b0da644f1782...| 5.0| 79.23| 1|
|9680a78df870a1cb6...| 4.0| 108.63| 1|
|f34152d311875e9da...| 5.0| 46.61| 1|
|b914a18d4547eff29...| 1.0| 767.33| 1|
|af407c297ba8ec158...| 5.0| 144.46| 1|
|192d363be840c4c30...| 5.0| 66.71| 1|
|b05fae603a3a28a97...| 4.0| 316.4| 1|
|ee08e2b1f2f38ce75...| 5.0| 51.06| 1|
|02a97df83a8a100c7...| 1.0| 78.72| 1|
|176faef125e3675b4...| 1.0| 44.27| 1|
|3ecdc0c6f18cf5c74...| 5.0| 149.01| 1|
|4ecb94d07e0dc5367...| 5.0| 264.33| 1|
|a6a8b6b05afd3a6e6...| 1.0| 109.24| 1|
|12803e6c487560862...| 5.0| 2223.12| 1|
|f2a286e75c6c6022c...| 1.0| 191.79| 1|
|481a7cf644601696c...| 4.0| 209.78| 1|
|b9732f4c0f2003913...| 3.0| 138.25| 1|
|5f82c0dd37c20e3f5...| 5.0| 313.67| 1|
+--------------------+----------+---------+-------------+
*/
// Product Category Analysis:
// --------------------------
val pCatRdd=ePairRdd.map{case(oid,cid,qt,cp,sp,ts,rating,pCat,pId,sp_type,oStat,pWt,pLen,pHt,pWidth,cCity,cState,sId,sCity,sState,sInstal)=>
(pCat,(List(rating),List(sp)))}.reduceByKey{case(a,b)=>(a._1++b._1,a._2++b._2)}.map{case(a,(b,c))=>(a,b.sum*1.0/b.length,c.sum/c.length,c.length)}
//Total number of unique product categories sold
pCatRdd.count
//res23: Long = 66
pCatRdd.toDF("Product Category","Avg Rating","Avg Sales","No. of Orders").show
/*
+--------------------+------------------+----------+-------------+
| Product Category| Avg Rating| Avg Sales|No. of Orders|
+--------------------+------------------+----------+-------------+
| party_supplies|3.1666666666666665| 99.714165| 12|
| dvds_blu_ray| 2.0| 87.973335| 3|
|agro_industry_and...| 3.6| 394.45065| 15|
| cds_dvds_musicals| 5.0| 117.58| 1|
| computers| 3.96| 1895.7657| 25|
|books_general_int...| 4.085106382978723|113.190865| 47|
| bed_bath_table|3.5972558514931396| 158.00053| 1239|
| telephony| 3.719730941704036| 144.22685| 446|
|fashion_childrens...| 5.0| 97.48| 1|
| diapers_and_hygiene| 3.5| 74.175| 4|
| audio|3.8095238095238093| 139.87924| 42|
| books_technical| 3.9| 72.131004| 40|
| small_appliances|3.6779661016949152| 357.91623| 59|
|fashion_underwear...| 4.571428571428571| 88.44857| 7|
| home_appliances|3.9831932773109244| 89.1548| 119|
| food| 4.029850746268656| 87.38002| 67|
| stationery| 4.278301886792453| 131.34314| 212|
| cool_stuff| 3.89237668161435| 229.74574| 223|
|small_appliances_...| 4.266666666666667| 481.29736| 15|
| christmas_supplies| 4.533333333333333| 89.307335| 15|
+--------------------+------------------+----------+-------------+
*/
val cSorted=pCatRdd.sortBy( x => (x._4), ascending = true).collect.toList
//Top 10 categories sold
cSorted.reverse.toDF("Product Category","Avg Rating","Avg Sales","No. of Orders").show
/*
+--------------------+------------------+----------+-------------+
| Product Category| Avg Rating| Avg Sales|No. of Orders|
+--------------------+------------------+----------+-------------+
| bed_bath_table|3.5972558514931396| 158.00053| 1239|
| health_beauty|3.8967052537845057| 179.26001| 1123|
| housewares|3.6645569620253164| 184.62833| 948|
| watches_gifts| 3.685774946921444| 220.68384| 942|
| sports_leisure|3.9197860962566846| 158.82326| 748|
| furniture_decor| 3.590659340659341| 199.38974| 728|
|computers_accesso...| 3.654879773691655| 188.66545| 707|
| auto| 3.810313075506446| 190.96826| 543|
| telephony| 3.719730941704036| 144.22685| 446|
| garden_tools| 4.0625| 195.06085| 384|
| baby|3.6064139941690962| 216.75752| 343|
| electronics|3.9792387543252596|105.032196| 289|
| perfumery| 4.020833333333333| 160.43077| 288|
| toys| 3.928030303030303| 177.35278| 264|
| pet_shop|4.0627450980392155| 177.15346| 255|
| cool_stuff| 3.89237668161435| 229.74574| 223|
| stationery| 4.278301886792453| 131.34314| 212|
| office_furniture| 3.018957345971564| 461.32| 211|
|construction_tool...|3.7923497267759565| 268.34167| 183|
|fashion_bags_acce...| 4.073033707865169| 139.55518| 178|
+--------------------+------------------+----------+-------------+
*/
val file = new java.io.PrintStream("BDA_Project\\Category_analysis.csv")
pCatRdd.sortBy(_._4,false).collect.foreach ( x => file.println(x._1+","+x._2+","+x._3+","+x._4) )
file.close
//Least 10 categories sold
cSorted.toDF("Product Category","Avg Rating","Avg Sales","No. of Orders").show
/*
+--------------------+------------------+---------+-------------+
| Product Category| Avg Rating|Avg Sales|No. of Orders|
+--------------------+------------------+---------+-------------+
| cds_dvds_musicals| 5.0| 117.58| 1|
|fashion_childrens...| 5.0| 97.48| 1|
| flowers| 3.0| 35.72| 1|
|tablets_printing_...| 5.0| 58.19| 1|
| dvds_blu_ray| 2.0|87.973335| 3|
| diapers_and_hygiene| 3.5| 74.175| 4|
| books_imported| 3.75| 221.2775| 4|
| home_comfort_2| 2.0|52.300003| 4|
|fashion_underwear...| 4.571428571428571| 88.44857| 7|
|arts_and_craftman...| 3.0|111.47876| 8|
| music|3.7777777777777777| 252.8711| 9|
|fashion_male_clot...| 4.333333333333333|123.42113| 9|
| party_supplies|3.1666666666666665|99.714165| 12|
| cine_photo| 4.153846153846154|117.23462| 13|
| furniture_bedroom| 4.153846153846154| 260.3946| 13|
|agro_industry_and...| 3.6|394.45065| 15|
|small_appliances_...| 4.266666666666667|481.29736| 15|
| christmas_supplies| 4.533333333333333|89.307335| 15|
|costruction_tools...| 4.266666666666667|310.21066| 15|
| fashion_shoes| 4.75|163.08998| 16|
+--------------------+------------------+---------+-------------+
*/
//Ratings given
val ratingRdd=ePairRdd.map{case(oid,cid,qt,cp,sp,ts,rating,pCat,pId,sp_type,oStat,pWt,pLen,pHt,pWidth,cCity,cState,sId,sCity,sState,sInstal)=>
(rating,1)}.reduceByKey(_+_)
//ratingRdd: org.apache.spark.rdd.RDD[(Int, Int)] = ShuffledRDD[98] at reduceByKey at <console>:26
ratingRdd.sortBy(x=>x._1).toDF("Rating","Count of Ratings").show
/*
+------+----------------+
|Rating|Count of Ratings|
+------+----------------+
| 1| 2244|
| 2| 609|
| 3| 945|
| 4| 1743|
| 5| 6190|
+------+----------------+
*/
val file = new java.io.PrintStream("BDA_Project\\Ratings_count.csv")
ratingRdd.sortBy(x=>x._1).collect.foreach{x=> file.println(x._1+","+x._2)}
file.close
// Payment prefernce:
// ------------------
val ePairRdd7 = ePairRdd.map{ case(oid,cid,qt,cp,sp,ts,rating,pCat,pId,sp_type,
oStat,pWt,pLen,pHt,pWidth,cCity,
cState,sId,sCity,sState,sInstal) => (sp_type,1) }.reduceByKey(_+_).sortBy(_._2,false)
val ePairRdd8 = ePairRdd.map{ case(oid,cid,qt,cp,sp,ts,rating,pCat,pId,sp_type,
oStat,pWt,pLen,pHt,pWidth,cCity,
cState,sId,sCity,sState,sInstal) => (sp_type,sp*qt) }.reduceByKey(_+_).sortBy(_._2,false)
// Joining 2 RDDs
val ePairRdd10 = ePairRdd7.join(ePairRdd8).map{ case(sp_type,q_p)=>
val qt = q_p._1
val price = q_p._2
(sp_type,qt,price) }.sortBy(_._2,false)
// Most commonly used payment types:
val ePairRdd11 = ePairRdd10.map{ case(sp_type,qt,price)=>
val avg_price = price/qt
(sp_type,qt,price,avg_price) }
ePairRdd11.toDF("Payment Type","Count","Total Amount spent","Avg Amount spent per order").show
/*
+------------+-----+------------------+--------------------------+
|Payment Type|Count|Total Amount spent|Avg Amount spent per order|
+------------+-----+------------------+--------------------------+
| credit_card| 8786| 2526096.8| 287.51385|
| boleto| 2118| 925155.56| 436.8062|
| voucher| 515| 42341.64| 82.21678|
| debit_card| 312| 63289.8| 202.85193|
+------------+-----+------------------+--------------------------+
*/
val file = new java.io.PrintStream("BDA_Project\\payment_preference.csv")
ePairRdd11.sortBy(_._2,false).collect.foreach ( x => file.println(x._1+","+x._2+","+x._3+","+x._4) )
file.close
// Count of Orders With each No. of Payment Installments:
val ePairRdd12 = ePairRdd.map{ case(oid,cid,qt,cp,sp,ts,rating,pCat,pId,sp_type,
oStat,pWt,pLen,pHt,pWidth,cCity,
cState,sId,sCity,sState,sInstal) => (sInstal,1) }.reduceByKey(_+_).sortBy(_._1)
ePairRdd12.toDF("No. of Payment Installments","Count for each installment").show
/*
+---------------------------+--------------------------+
|No. of Payment Installments|Count for each installment|
+---------------------------+--------------------------+
| 1| 5669|
| 2| 1345|
| 3| 1150|
| 4| 812|
| 5| 601|
| 6| 503|
| 7| 176|
| 8| 667|
| 9| 60|
| 10| 697|
| 11| 1|
| 12| 18|
| 13| 3|
| 14| 2|
| 15| 12|
| 16| 4|
| 17| 1|
| 18| 8|
| 20| 2|
+---------------------------+--------------------------+
*/
val file = new java.io.PrintStream("BDA_Project\\no_of_installment.csv")
ePairRdd12.collect.foreach ( file.println(_) )
file.close
// Seller Ranking:
// ---------------
val ePairRdd21 = ePairRdd.map{ case(oid,cid,qt,cp,sp,ts,rating,pCat,pId,sp_type,
oStat,pWt,pLen,pHt,pWidth,cCity,
cState,sId,sCity,sState,sInstal) => (sId,1) }.reduceByKey(_+_).sortBy(_._2,false)
val ePairRdd22 = ePairRdd.map{ case(oid,cid,qt,cp,sp,ts,rating,pCat,pId,sp_type,
oStat,pWt,pLen,pHt,pWidth,cCity,
cState,sId,sCity,sState,sInstal) => (sId,sp*qt) }.reduceByKey(_+_).sortBy(_._2,false)
val ePairRdd23 = ePairRdd.map{ case(oid,cid,qt,cp,sp,ts,rating,pCat,pId,sp_type,
oStat,pWt,pLen,pHt,pWidth,cCity,
cState,sId,sCity,sState,sInstal) => (sId,rating) }.reduceByKey(_+_).sortBy(_._2,false)
// Joining 3 RDDs
val ePairRdd24 = (ePairRdd21.join(ePairRdd22)).join(ePairRdd23)
ePairRdd24.toDF.show
val ePairRdd25 = ePairRdd24.map{ case(sId,q_p_r)=>
val q_p = q_p_r._1
val qt = q_p._1
val price = q_p._2
val rating_all = q_p_r._2
val rating = rating_all.toFloat/qt.toFloat
val rating_roundoff = (BigDecimal(rating).setScale(2, BigDecimal.RoundingMode.HALF_UP)).toFloat
(sId,qt,price,rating_roundoff) }.sortBy(_._3,false)
ePairRdd25.toDF("Seller ID","Customers Reached","Total Sales","Avg Rating").show
/*
+--------------------+-----------------+-----------+----------+
| Seller ID|Customers Reached|Total Sales|Avg Rating|
+--------------------+-----------------+-----------+----------+
|7c67e1448b00f6e96...| 170| 186788.0| 2.84|
|1f50f920176fa81da...| 189| 142442.4| 4.14|
|955fee9216a65b617...| 232| 102963.87| 3.71|
|1025f0e2d44d7041d...| 238| 76052.0| 3.48|
|52d76513f0c4d97f3...| 12| 73640.055| 3.0|
|b37c4c02bda3161a7...| 4| 72748.8| 1.0|
|9803a40e82e45418a...| 11| 67857.95| 2.0|
|7681ef142fd2c1904...| 28| 62967.66| 4.29|
|fcdd820084f17e998...| 13| 51271.54| 2.08|
|de722cd6dad950a92...| 70| 45660.85| 4.06|
|e7d5b006eb624f130...| 14| 44268.84| 2.86|
|ca3bd7cd9f149df75...| 21| 43684.7| 3.1|
|6560211a19b47992c...| 316| 34781.816| 3.58|
|da8622b14eb17ae28...| 128| 33521.6| 4.0|
|7d13fca1522535862...| 158| 33313.93| 3.74|
|f7ba60f8c3f99e7ee...| 28| 32147.6| 4.0|
|634964b17796e6430...| 57| 31873.389| 3.68|
|4a3ca9315b744ce9f...| 145| 30838.37| 3.77|
|c70c1b0d8ca86052f...| 86| 29413.35| 3.63|
|1a932caad4f9d8040...| 12| 28909.49| 3.17|
+--------------------+-----------------+-----------+----------+
*/
val file = new java.io.PrintStream("BDA_Project\\seller_rank.csv")
ePairRdd25.collect.foreach ( file.println(_) )
file.close
// Potential Customer Location: - Statewise
// ----------------------------
val ePairRdd13 = ePairRdd.map{ case(oid,cid,qt,cp,sp,ts,rating,pCat,pId,sp_type,
oStat,pWt,pLen,pHt,pWidth,cCity,
cState,sId,sCity,sState,sInstal) => (cState,1) }.reduceByKey(_+_).sortBy(_._2,false)
val ePairRdd14 = ePairRdd.map{ case(oid,cid,qt,cp,sp,ts,rating,pCat,pId,sp_type,
oStat,pWt,pLen,pHt,pWidth,cCity,
cState,sId,sCity,sState,sInstal) => (cState,sp*qt) }.reduceByKey(_+_).sortBy(_._2,false)
// Joining 2 RDDs
val ePairRdd15 = ePairRdd13.join(ePairRdd14).map{ case(cState,q_p)=>
val qt = q_p._1
val price = q_p._2
(cState,qt,price) }.sortBy(_._2,false)
val ePairRdd16 = ePairRdd15.map{ case(cState,qt,price)=>
val avg_price = price/qt
(cState,qt,price,avg_price) }.sortBy(_._3,false)
ePairRdd16.toDF("Customer State","Count","Total Amount spent","Avg Amount spent per order").show
/*
+--------------+-----+------------------+--------------------------+
|Customer State|Count|Total Amount spent|Avg Amount spent per order|
+--------------+-----+------------------+--------------------------+
| SP| 5103| 1246241.2| 244.21738|
| RJ| 1501| 435567.5| 290.18488|
| MG| 1264| 359334.5| 284.28363|
| SC| 376| 231889.25| 616.72675|
| PR| 509| 167442.97| 328.96457|
| RS| 520| 161983.2| 311.50616|
| ES| 248| 133218.7| 537.1722|
| BA| 459| 128706.6| 280.40652|
| MT| 135| 109549.266| 811.476|
| GO| 228| 81480.17| 357.36917|
| MA| 67| 79351.27| 1184.3474|
| DF| 258| 75682.586| 293.34335|
| PE| 235| 65884.42| 280.35925|
| CE| 168| 61359.96| 365.23785|
| PA| 145| 55289.375| 381.30603|
| PB| 79| 34791.336| 440.39667|
| MS| 94| 28896.066| 307.40497|
| RN| 78| 28002.26| 359.00333|
| PI| 68| 18896.61| 277.89133|
| TO| 38| 14490.5| 381.32895|
+--------------+-----+------------------+--------------------------+
*/
val file = new java.io.PrintStream("BDA_Project\\statewise_sales.csv")
ePairRdd16.collect.foreach{x=> file.println(x._1+","+x._2+","+x._3+","+x._4)}
file.close
// Potential Customer Location: - Citywise
// ----------------------------
val ePairRdd17 = ePairRdd.map{ case(oid,cid,qt,cp,sp,ts,rating,pCat,pId,sp_type,
oStat,pWt,pLen,pHt,pWidth,cCity,
cState,sId,sCity,sState,sInstal) => (cCity,1) }.reduceByKey(_+_).sortBy(_._2,false)
val ePairRdd18 = ePairRdd.map{ case(oid,cid,qt,cp,sp,ts,rating,pCat,pId,sp_type,
oStat,pWt,pLen,pHt,pWidth,cCity,
cState,sId,sCity,sState,sInstal) => (cCity,sp*qt) }.reduceByKey(_+_).sortBy(_._2,false)
// Joining 2 RDDs
val ePairRdd19 = ePairRdd17.join(ePairRdd18).map{ case(cCity,q_p)=>
val qt = q_p._1
val price = q_p._2
(cCity,qt,price) }.sortBy(_._2,false)
val ePairRdd20 = ePairRdd19.map{ case(cCity,qt,price)=>
val avg_price = price/qt
(cCity,qt,price,avg_price) }.sortBy(_._3,false)
ePairRdd20.toDF("Customer City","Count","Total Amount spent","Avg Amount spent per order").show
/*
+--------------------+-----+------------------+--------------------------+
| Customer City|Count|Total Amount spent|Avg Amount spent per order|
+--------------------+-----+------------------+--------------------------+
| sao paulo| 1917| 475675.66| 248.13545|
| rio de janeiro| 788| 253107.45| 321.20236|
| celso ramos| 12| 97263.66| 8105.3047|
| vila velha| 56| 85253.87| 1522.3905|
| brasilia| 258| 75682.586| 293.34335|
| belo horizonte| 312| 73878.53| 236.79016|
| salvador| 181| 72391.22| 399.95148|
| sao luis| 30| 70746.414| 2358.2139|
|campo novo do par...| 7| 68208.54| 9744.077|
| valinhos| 41| 50681.918| 1236.1443|
| fortaleza| 87| 41759.633| 479.9958|
| curitiba| 137| 39034.86| 284.926|
| guarulhos| 153| 36826.586| 240.69664|
| rio verde| 19| 36516.27| 1921.9089|
| campinas| 161| 34916.57| 216.87311|
| porto alegre| 128| 33106.09| 258.64133|
| santos| 96| 31323.637| 326.28787|
| colombo| 13| 30087.07| 2314.3901|
| joao pessoa| 37| 24550.9| 663.53784|
| araucaria| 12| 23411.39| 1950.9492|
+--------------------+-----+------------------+--------------------------+
*/
val file = new java.io.PrintStream("BDA_Project\\city_wise_sales.csv")
ePairRdd20.collect.foreach{x=> file.println(x._1+","+x._2+","+x._3+","+x._4)}
file.close
// Logistics Based Optimization Insights:
// --------------------------------------
// Which city buys more heavy wieght products?
val city_weight = ePairRdd.map{ case(oid,cid,qt,cp,sp,ts,rating,pCat,pId,sp_type,oStat,pWt,pLen,pHt,pWidth,cCity,cState,sId,sCity,sState,sInstal) =>
var no_of_w = 0
if ((pWt<3000)&&(pWt>=1000)) {no_of_w += 1}
else if ((pWt<1000)&&(pWt>=200)) {no_of_w += 2}
else if (pWt<200) {no_of_w += 3}
else {no_of_w += 4}
(cCity,List(no_of_w)) }.reduceByKey(_++_)
val city_weight_2 = city_weight.map{ case(cCity,weight_list) =>
val collection = weight_list
val new_collection = collection.map(x => (x,1))
val res = new_collection.reduce( (a,b) => ( a._1 + b._1,a._2 + b._2 ) )
val avg_weight = (res._1/res._2).toFloat
(cCity,avg_weight) }
city_weight_2.toDF("City","Weight Category").show
val city_weight_label = city_weight_2.map{ case(cCity,weight) =>
var label = "None"
if (weight==1) label="Heavy"
else if (weight==2) label="Slightly Heavy"
else if (weight==2) label="Medium"
else label="Light"
(cCity,label) }
val city_weight_list = city_weight_label.map{ case(cCity,label) =>
(label,List(cCity))}.reduceByKey(_++_).map{ case(label,cCity_list) =>
(label,cCity_list,cCity_list.size) }
city_weight_list.toDF("Weight Category","City","City Count").show
/*
+---------------+--------------------+----------+
|Weight Category| City|City Count|
+---------------+--------------------+----------+
| Light|[sao gabriel da p...| 323|
| Heavy|[bandeirantes, ca...| 389|
| Slightly Heavy|[sao francisco de...| 873|
+---------------+--------------------+----------+
*/
val file = new java.io.PrintStream("BDA_Project\\city_weight_based.csv")
city_weight_list.collect.foreach ( file.println(_) )
file.close
// How many proucts are domestic sales(Sold within seller city itself)?
val sales_loc = ePairRdd.map{ case(oid,cid,qt,cp,sp,ts,rating,pCat,pId,sp_type,oStat,pWt,pLen,pHt,pWidth,cCity,cState,sId,sCity,sState,sInstal) =>
var count = 0
if (cCity == sCity) count += 1
(cCity,sCity,count) }.filter(x => (x._1 == x._2)).map{ x=> (x._1,x._3) }.reduceByKey(_+_)
sales_loc.toDF("City","Count").show
/*
+--------------------+---+
| _1| _2|
+--------------------+---+
| salvador| 1|
| fortaleza| 1|
| sao luis| 2|
| goiania| 1|
| nova iguacu| 3|
| contagem| 1|
| campinas| 9|
| porto alegre| 1|
| sao paulo|542|
| joinville| 1|
| guarulhos| 10|
| belo horizonte| 10|
| sao jose| 1|
| santos| 1|
| sao jose dos campos| 2|
| santo andre| 2|
| curitiba| 13|
| limeira| 1|
|sao bernardo do c...| 1|
| sumare| 1|
+--------------------+---+
*/
val file = new java.io.PrintStream("BDA_Project\\domestic_sales.csv")
sales_loc.collect.foreach ( file.println(_) )
file.close
// Domestic vs Foreign sales:
val domestic_sales_count = sales_loc.map{ x => (x._2)}.sum.toInt
val foriegn_sales_count = (ePairRdd.count - domestic_sales_count).toInt
val location_count = sc.parallelize( Array((domestic_sales_count,foriegn_sales_count)) )
location_count.toDF("Domestic Sales","Foreign Sales").show
/*
+--------------+-------------+
|Domestic Sales|Foreign Sales|
+--------------+-------------+
| 650| 11081|
+--------------+-------------+
*/
val file = new java.io.PrintStream("BDA_Project\\location_sales.csv")
location_count.collect.foreach ( file.println(_) )
file.close
// Predicting future sales:
// ------------------------
// Note: Predicting Sales Based on Hour, Day & Month
import org.apache.spark.ml.regression.LinearRegression
import org.apache.spark.ml.feature.VectorAssembler
import org.apache.spark.ml.feature.Normalizer
val inputRdd = ePairRdd.map{ case(oid,cid,qt,cp,sp,ts,rating,pCat,pId,sp_type,oStat,pWt,pLen,pHt,pWidth,cCity,cState,sId,sCity,sState,sInstal) =>
val ds_ts = ts.split(" ")
val d_m_y = ds_ts(0).split("-")
val hour = ds_ts(1).split(":")
(sp.toFloat, hour(0).toInt,d_m_y(0).toInt,d_m_y(1).toInt) }.sortBy(_._1,false)
inputRdd.toDF("Sales","Hour","Day","Month").show
/*
+--------+----+---+-----+
| Sales|Hour|Day|Month|
+--------+----+---+-----+
|13664.08| 15| 29| 9|
|13664.08| 15| 29| 9|
|13664.08| 15| 29| 9|
|13664.08| 15| 29| 9|
|13664.08| 15| 29| 9|
|13664.08| 15| 29| 9|
|13664.08| 15| 29| 9|
|13664.08| 15| 29| 9|
| 7274.88| 14| 15| 7|
| 7274.88| 14| 15| 7|
| 7274.88| 14| 15| 7|
| 7274.88| 14| 15| 7|
| 6929.31| 20| 12| 2|
| 6922.21| 18| 25| 7|
| 6726.66| 18| 24| 5|
| 6081.54| 11| 24| 11|
| 6081.54| 11| 24| 11|
| 6081.54| 11| 24| 11|
| 6081.54| 11| 24| 11|
| 6081.54| 11| 24| 11|
+--------+----+---+-----+
*/
inputRdd.count
/*
res29: Long = 116573
*/
val df = inputRdd.toDF("label","Hour","Day","Month")
val assembler1 = new VectorAssembler().
setInputCols(Array("Hour","Day","Month")).
setOutputCol("features").
transform(df)
assembler1.show()
/*
+--------+----+---+-----+----------------+
| label|Hour|Day|Month| features|
+--------+----+---+-----+----------------+
|13664.08| 15| 29| 9| [15.0,29.0,9.0]|
|13664.08| 15| 29| 9| [15.0,29.0,9.0]|
|13664.08| 15| 29| 9| [15.0,29.0,9.0]|
|13664.08| 15| 29| 9| [15.0,29.0,9.0]|
|13664.08| 15| 29| 9| [15.0,29.0,9.0]|
|13664.08| 15| 29| 9| [15.0,29.0,9.0]|
|13664.08| 15| 29| 9| [15.0,29.0,9.0]|
|13664.08| 15| 29| 9| [15.0,29.0,9.0]|
| 7274.88| 14| 15| 7| [14.0,15.0,7.0]|
| 7274.88| 14| 15| 7| [14.0,15.0,7.0]|
| 7274.88| 14| 15| 7| [14.0,15.0,7.0]|
| 7274.88| 14| 15| 7| [14.0,15.0,7.0]|
| 6929.31| 20| 12| 2| [20.0,12.0,2.0]|
| 6922.21| 18| 25| 7| [18.0,25.0,7.0]|
| 6726.66| 18| 24| 5| [18.0,24.0,5.0]|
| 6081.54| 11| 24| 11|[11.0,24.0,11.0]|
| 6081.54| 11| 24| 11|[11.0,24.0,11.0]|
| 6081.54| 11| 24| 11|[11.0,24.0,11.0]|
| 6081.54| 11| 24| 11|[11.0,24.0,11.0]|
| 6081.54| 11| 24| 11|[11.0,24.0,11.0]|
+--------+----+---+-----+----------------+
*/
val normalizer = new Normalizer().
setInputCol("features").
setOutputCol("normFeatures").
setP(2.0).
transform(assembler1)
normalizer.show()
/*
+--------+----+---+-----+----------------+--------------------+
| label|Hour|Day|Month| features| normFeatures|
+--------+----+---+-----+----------------+--------------------+
|13664.08| 15| 29| 9| [15.0,29.0,9.0]|[0.44290394651811...|
|13664.08| 15| 29| 9| [15.0,29.0,9.0]|[0.44290394651811...|
|13664.08| 15| 29| 9| [15.0,29.0,9.0]|[0.44290394651811...|
|13664.08| 15| 29| 9| [15.0,29.0,9.0]|[0.44290394651811...|
|13664.08| 15| 29| 9| [15.0,29.0,9.0]|[0.44290394651811...|
|13664.08| 15| 29| 9| [15.0,29.0,9.0]|[0.44290394651811...|
|13664.08| 15| 29| 9| [15.0,29.0,9.0]|[0.44290394651811...|
|13664.08| 15| 29| 9| [15.0,29.0,9.0]|[0.44290394651811...|
| 7274.88| 14| 15| 7| [14.0,15.0,7.0]|[0.64577184562021...|
| 7274.88| 14| 15| 7| [14.0,15.0,7.0]|[0.64577184562021...|
| 7274.88| 14| 15| 7| [14.0,15.0,7.0]|[0.64577184562021...|
| 7274.88| 14| 15| 7| [14.0,15.0,7.0]|[0.64577184562021...|
| 6929.31| 20| 12| 2| [20.0,12.0,2.0]|[0.85435765771676...|
| 6922.21| 18| 25| 7| [18.0,25.0,7.0]|[0.56978004404962...|
| 6726.66| 18| 24| 5| [18.0,24.0,5.0]|[0.59183635429928...|
| 6081.54| 11| 24| 11|[11.0,24.0,11.0]|[0.38460598070128...|
| 6081.54| 11| 24| 11|[11.0,24.0,11.0]|[0.38460598070128...|
| 6081.54| 11| 24| 11|[11.0,24.0,11.0]|[0.38460598070128...|
| 6081.54| 11| 24| 11|[11.0,24.0,11.0]|[0.38460598070128...|
| 6081.54| 11| 24| 11|[11.0,24.0,11.0]|[0.38460598070128...|
+--------+----+---+-----+----------------+--------------------+
*/
val Array(trainingData, testData) = normalizer.randomSplit(Array(0.70, 0.30))
trainingData.count
/*
Long = 81922
*/
testData.count
/*
Long = 34651
*/
val lr = new LinearRegression().
setLabelCol("label").
setFeaturesCol("normFeatures").
setMaxIter(10).
setRegParam(1.0).
setElasticNetParam(1.0)
val lrModel = lr.fit(trainingData)
lrModel.
transform(testData).
select("features","label","prediction").
show()
/*
+----------------+------+------------------+
| features| label| prediction|
+----------------+------+------------------+
| [20.0,5.0,5.0]|105.95|172.54876571676618|
| [20.0,6.0,6.0]|105.95|172.97477934217812|
| [10.0,14.0,9.0]|105.96|174.94695419232792|
| [21.0,3.0,1.0]|105.99|170.56329677228612|
| [11.0,31.0,7.0]|106.02| 172.2586847169489|
| [15.0,29.0,5.0]|106.02| 171.661097436566|
| [21.0,13.0,2.0]|106.02| 170.9172572137698|
| [10.0,6.0,3.0]|106.03|172.69018221534373|
| [8.0,16.0,6.0]|106.06| 173.4150760503963|
| [11.0,7.0,3.0]|106.07|172.42800714769788|
|[14.0,30.0,11.0]|106.07|173.38683990123567|
| [21.0,12.0,6.0]|106.07|172.60212984586786|
|[23.0,26.0,11.0]|106.07|173.24749127118145|
| [23.0,3.0,1.0]|106.08| 170.5210312172166|
|[11.0,29.0,11.0]| 106.1|173.58619736348862|