Apache Spark Machine Learning

Predicting Possible Loan Default Using Machine Learning

Predicting Possible Loan Default Using Machine Learning

Project idea – The idea behind this ML project is to build a model for a Loan Prediction Based on Customer Behavior and determine the risk factor. Problem Statement or Business Problem About CompanyWonderful Dream Housing Finance company deals in all home loans. this ML project is to build a model for a Loan Prediction Based on Customer BehaviorProblemCompany wants to automate the loan risk factor based on customer detail behavior. A loan default occurs when a borrower takes money from a bank and does not repay the loan. Details are Income, Age, Experience, Married/Single, House_Ownership, Car Ownership, Profession, City,…
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Machine Learning Project – Loan Approval Prediction

Machine Learning Project – Loan Approval Prediction

Project idea – The idea behind this ML project is to build a model for a Home Loan Company to validates the customer eligibility for loan. Problem Statement or Business Problem About CompanyWonderful Dream Housing Finance company deals in all home loans. They have presence across all urban, semi urban and rural areas. Customer first apply for home loan after that company validates the customer eligibility for loan.ProblemCompany wants to automate the loan eligibility process (real time) based on customer detail provided while filling online application form. These details are Gender, Marital Status, Education, Number of Dependents, Income, Loan Amount,…
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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=pcap-shape: bell=b,conical=c,convex=x,flat=f, knobbed=k,sunken=scap-surface: fibrous=f,grooves=g,scaly=y,smooth=scap-color: brown=n, buff=b, cinnamon=c, gray=g,green=r, pink=p, purple=u, red=e,white=w,yellow=ybruises: bruises=t,no=fodor: almond=a, anise=l, creosote=c, fishy=y, foul=f, musty=m, none=n, pungent=p,spicy=sgill-attachment: attached=a,descending=d,free=f,notched=ngill-spacing: close=c,crowded=w,distant=dgill-size: broad=b,narrow=ngill-color: black=k,brown=n,buff=b,chocolate=h,gray=g, green=r, orange=o, pink=p,purple=u,red=e,white=w,yellow=ystalk-shape: enlarging=e,tapering=tstalk-root: bulbous=b, club=c, cup=u, equal=e, rhizomorphs=z,…
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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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Machine Learning Project – Predict Forest Cover Part 1

Machine Learning Project – Predict Forest Cover Part 1

In this project, we will predict Forest Cover based on various attributes (cartographic variables) of the Forest. Hence, this is a classification problem. Problem Statement or Business Problem In this project, we'll predict Forest Cover supported various attributes (cartographic variables) of the Forest. Hence, this is often a classification problem. Attribute Information or Dataset Details: Given is the attribute name, attribute type, the measurement unit, and a brief description. The forest cover type is the classification problem. The order of this listing corresponds to the order of numerals along the rows of the database. NameData TypeMeasurementDescriptionElevationquantitativemetersElevation in metersAspectquantitativeazimuthAspect in degrees…
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