Spark SQL

Marketing Analytics Part 1

Marketing Analytics Part 1

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…
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Marketing Analytics Part 2

Marketing Analytics Part 2

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…
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Marketing Analytics Part 3

Marketing Analytics Part 3

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,…
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Customer Segmentation using Machine Learning in Apache Spark

Customer Segmentation using Machine Learning in Apache Spark

Customer segmentation is the practice of dividing a company's customers into groups that reflect similarities among customers in each group. The goal of segmenting customers is to decide how to relate to customers in each segment in order to maximize the value of each customer to the business. Problem Statement or Business Problem In this project, we will perform one of the most essential applications of machine learning – Customer Segmentation. We will implement customer segmentation in Apache Spark and Scala, whenever you need to find your best customer. Customer Segmentation is one of the most important applications of unsupervised…
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Machine Learning Project – Creating Movies Recommendation Engine using Apache Spark

Machine Learning Project – Creating Movies Recommendation Engine using Apache Spark

Movies are loved by everyone irrespective of age, gender, race, color, or geographical location. A recommendation system is a filtration program whose prime goal is to predict the “rating” or “preference” of a user towards a domain-specific item or item. Recommendation systems encompass a class of techniques and algorithms that can suggest “relevant” items to users. They predict future behavior based on past data through a multitude of techniques. Problem Statement or Business Problem In this project, we will generate top 10 movie recommendations for each user as well as generate top 10 user recommendations for each movie. Attribute Information…
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Machine Learning Project on Sales Prediction or Sale Forecast

Machine Learning Project on Sales Prediction or Sale Forecast

Sales forecasting is the process of estimating future sales. Accurate sales forecasts enable companies to make informed business decisions and predict short-term and long-term performance. Companies can base their forecasts on past sales data, industry-wide comparisons, and economic trends. It is easier for established companies to predict future sales based on years of past business data. Newly founded companies have to base their forecasts on less-verified information, such as market research and competitive intelligence to forecast their future business. Sales forecasting gives insight into how a company should manage its workforce, cash flow, and resources. In addition to helping a…
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Machine Learning Project on Mushroom Classification whether it’s edible or poisonous Part 1

Machine Learning Project on Mushroom Classification whether it’s edible or poisonous Part 1

A mushroom, or toadstool, is the fleshy, spore-bearing fruiting body of a fungus, typically produced above ground on soil or on its food source. Problem Statement or Business Problem In this project, looking at the various properties of a mushroom, we will predict whether the mushroom is edible or poisonous. Attribute Information or Dataset Details: To be more understandable, let's write properties one by one. classes: edible=e, poisonous=p cap-shape: bell=b,conical=c,convex=x,flat=f, knobbed=k,sunken=s cap-surface: fibrous=f,grooves=g,scaly=y,smooth=s cap-color: brown=n, buff=b, cinnamon=c, gray=g,green=r, pink=p, purple=u, red=e,white=w,yellow=y bruises: bruises=t,no=f odor: almond=a, anise=l, creosote=c, fishy=y, foul=f, musty=m, none=n, pungent=p,spicy=s gill-attachment: attached=a,descending=d,free=f,notched=n gill-spacing: close=c,crowded=w,distant=d gill-size: broad=b,narrow=n gill-color: black=k,brown=n,buff=b,chocolate=h,gray=g,…
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Machine Learning Project on Mushroom Classification whether it’s edible or poisonous Part 2

Machine Learning Project on Mushroom Classification whether it’s edible or poisonous Part 2

Collecting all String Columns into an Array %scala var StringfeatureCol = Array("class", "capshape", "capsurface", "capcolor", "bruises", "odor", "gillattachment", "gillspacing", "gillsize", "gillcolor", "stalkshape", "stalkroot", "stalksurfaceabovering", "stalksurfacebelowring", "stalkcolorabovering", "stalkcolorbelowring", "veiltype", "veilcolor", "ringnumber", "ringtype", "sporeprintcolor", "population", "habitat") StringIndexer encodes a string column of labels to a column of label indices. Example of StringIndexer %scala import org.apache.spark.ml.feature.StringIndexer val df = spark.createDataFrame( Seq((0, "a"), (1, "b"), (2, "c"), (3, "a"), (4, "a"), (5, "c")) ).toDF("id", "category") df.show() val indexer = new StringIndexer() .setInputCol("category") .setOutputCol("categoryIndex") val indexed = indexer.fit(df).transform(df) indexed.show() Output: +---+--------+ | id|category| +---+--------+ | 0| a| | 1| b| | 2| c| | 3|…
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Machine Learning Pipeline Application on Power Plant. (Part 1)

Machine Learning Pipeline Application on Power Plant. (Part 1)

This is an end-to-end Project of performing Extract-Transform-Load and Exploratory Data Analysis on a real-world dataset, and then applying several different machine learning algorithms to solve a supervised regression problem on the dataset. Our goal is to accurately predict power output given a set of environmental readings from various sensors in a natural gas-fired power generation plant. Background Power generation is a complex process, and understanding and predicting power output is an important element in managing a plant and its connection to the power grid. The operators of a regional power grid create predictions of power demand based on historical…
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Machine Learning Pipeline Application on Power Plant. (Part 2)

Machine Learning Pipeline Application on Power Plant. (Part 2)

Visualize Your Data To understand our data, we will look for correlations between features and the label. This can be important when choosing a model. E.g., if features and a label are linearly correlated, a linear model like Linear Regression can do well; if the relationship is very non-linear, more complex models such as Decision Trees can be better. We can use Databrick's built in visualization to view each of our predictors in relation to the label column as a scatter plot to see the correlation between the predictors and the label. Exploratory Data Analysis (EDA) is an approach/philosophy for…
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