- Big Data Analytics Tutorial
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- Big Data Analytics - Overview
- Big Data Analytics - Data Life Cycle
- Big Data Analytics - Methodology
- Core Deliverables
- Key Stakeholders
- Big Data Analytics - Data Analyst
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- Big Data Analytics Project
- Data Analytics - Problem Definition
- Big Data Analytics - Data Collection
- Big Data Analytics - Cleansing data
- Big Data Analytics - Summarizing
- Big Data Analytics - Data Exploration
- Data Visualization

- Big Data Analytics Methods
- Big Data Analytics - Introduction to R
- Data Analytics - Introduction to SQL
- Big Data Analytics - Charts & Graphs
- Big Data Analytics - Data Tools
- Data Analytics - Statistical Methods

- Advanced Methods
- Machine Learning for Data Analysis
- Naive Bayes Classifier
- K-Means Clustering
- Association Rules
- Big Data Analytics - Decision Trees
- Logistic Regression
- Big Data Analytics - Time Series
- Big Data Analytics - Text Analytics
- Big Data Analytics - Online Learning

- Big Data Analytics Useful Resources
- Big Data Analytics - Quick Guide
- Big Data Analytics - Resources
- Big Data Analytics - Discussion

# Big Data Analytics - Text Analytics

In this chapter, we will be using the data scraped in the part 1 of the book. The data has text that describes profiles of freelancers, and the hourly rate they are charging in USD. The idea of the following section is to fit a model that given the skills of a freelancer, we are able to predict its hourly salary.

The following code shows how to convert the raw text that in this case has skills of a user in a bag of words matrix. For this we use an R library called tm. This means that for each word in the corpus we create variable with the amount of occurrences of each variable.

library(tm) library(data.table) source('text_analytics/text_analytics_functions.R') data = fread('text_analytics/data/profiles.txt') rate = as.numeric(data$rate) keep = !is.na(rate) rate = rate[keep] ### Make bag of words of title and body X_all = bag_words(data$user_skills[keep]) X_all = removeSparseTerms(X_all, 0.999) X_all # <<DocumentTermMatrix (documents: 389, terms: 1422)>> # Non-/sparse entries: 4057/549101 # Sparsity : 99% # Maximal term length: 80 # Weighting : term frequency - inverse document frequency (normalized) (tf-idf) ### Make a sparse matrix with all the data X_all <- as_sparseMatrix(X_all)

Now that we have the text represented as a sparse matrix we can fit a model that will give a sparse solution. A good alternative for this case is using the LASSO (least absolute shrinkage and selection operator). This is a regression model that is able to select the most relevant features to predict the target.

train_inx = 1:200 X_train = X_all[train_inx, ] y_train = rate[train_inx] X_test = X_all[-train_inx, ] y_test = rate[-train_inx] # Train a regression model library(glmnet) fit <- cv.glmnet(x = X_train, y = y_train, family = 'gaussian', alpha = 1, nfolds = 3, type.measure = 'mae') plot(fit) # Make predictions predictions = predict(fit, newx = X_test) predictions = as.vector(predictions[,1]) head(predictions) # 36.23598 36.43046 51.69786 26.06811 35.13185 37.66367 # We can compute the mean absolute error for the test data mean(abs(y_test - predictions)) # 15.02175

Now we have a model that given a set of skills is able to predict the hourly salary of a freelancer. If more data is collected, the performance of the model will improve, but the code to implement this pipeline would be the same.