Apache Spark

Health care analytics is the health care analysis activities that can be undertaken as a result of data collected from four areas within healthcare; claims and cost data, pharmaceutical and research and development (R&D) data, clinical data (collected from electronic medical records (EHRs)), and patient behavior and sentiment data. Data Description PatientProfile.csv – This file contains Patient profile details like PatientID, OnlineFollower, Social media details, Income, Education, Age, FirstInteractionDate, CityType and Employer_CategoryMore Info On patient_profiles file.Patient_ID Unique Identifier for each patient. This ID is not sequential in nature and can not be used in modelOnline_Follower Whether a patient follows MedCamp online or…

Patient's Age Patient's Income Patient's Occupation All in One Scatter Plot Loading Data into DataFrame %scala // File location and type val file_location = "/FileStore/tables/First_Health_Camp_Attended.csv" val file_type = "csv" // CSV options val infer_schema = "true" val first_row_is_header = "true" val delimiter = "," // The applied options are for CSV files. For other file types, these will be ignored. val First_Health_Camp_Attended = spark.read.format(file_type) .option("inferSchema", infer_schema) .option("header", first_row_is_header) .option("sep", delimiter) .load(file_location) display(First_Health_Camp_Attended) Count of Data (Total Records) %scala First_Health_Camp_Attended.count() res12: Long = 6218 Displaying Statistics of Data %scala display(First_Health_Camp_Attended.describe()) Print Schema of Data %scala First_Health_Camp_Attended.printSchema() root |-- Patient_ID: integer (nullable…

Marketing analytics consists of both qualitative and quantitative, structured and unstructured data used to drive strategic decisions in relation to brand and revenue outcomes. Overall goalYou're a marketing analyst and you've been told by the Chief Marketing Officer that recent marketing campaigns have not been as effective as they were expected to be. You need to analyze the data set to understand this problem and propose data-driven solutions.Section 01: Exploratory Data Analysis Are there any null values or outliers? How will you wrangle/handle them?Are there any variables that warrant transformations?Are there any useful variables that you can engineer with the given data?Do…

Are there any useful variables that you can engineer with the given data?Review a list of the feature names below, from which we can engineer:The total number of dependents in the home ('Dependents') can be engineered from the sum of 'Kidhome' and 'Teenhome'The year of becoming a customer ('Year_Customer') can be engineered from 'Dt_Customer'The total amount spent ('TotalMnt') can be engineered from the sum of all features containing the keyword 'Mnt'The total purchases ('TotalPurchases') can be engineered from the sum of all features containing the keyword 'Purchases' The total number of campaigns accepted ('TotalCampaignsAcc') can be engineered from the sum of…

NumStorePurchases VS MntGoldProds MntFishProducts Distribution Campaign 1 Campaign 2 Campaign 3 Campaign 4 Campaign 5 Section 03: Data Visualization Products VS Amount Spent Purchases Conclusion Recall the overall goal: You're a marketing analyst and you've been told by the Chief Marketing Officer that recent marketing campaigns have not been as effective as they were expected to be. You need to analyze the data set to understand this problem and propose data-driven solutions...Summary of actionable findings to improve advertising campaign success:Advertising campaign acceptance is positively correlated with income and negatively correlated with having kids/teensSuggested action: Create two streams of targeted advertising campaigns,…