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Reading list

Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

Data Pre-Processing: A Crucial Element of Analytics – Driven Embedded Systems

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Guest Blog Last Updated : 19 Dec, 2016
4 min read

Introduction

The goal of the Internet of Things (IoT) is to acquire data from various embedded systems and impart analytical processes on that data to improve performance, efficiency, and business outcomes. The ability to create analytics that process massive amounts of business and engineering data is enabling designers in many industries to develop intelligent products and services. Designers can use analytics to describe and predict a system’s behavior, and further combine analytics with embedded control systems to automate actions and decisions.

Whether cloud-based or embedded, the first step in developing analytics is to access the wealth of available data to explore patterns and develop deeper insights. This abundance of data is explored through data pre-processing, a crucial, yet often understated step in the creation of analytics-driven embedded systems.

 

Overview

Datasets can be large in size, come from many different sources and represent many different attributes. Therefore, the software tools used for exploratory analysis and analytics development should be capable of accessing all the data sources and formats that comprise the dataset. As real world data tends to be incomplete, noisy and inconsistent, data preparation is a very important procedure for both data warehousing and data mining.

Data preparation includes data cleaning, data integration, data transformation and data reduction. Data cleaning steps can be used to fill in missing values, smooth the noisy data, identify outliers and correct data inconsistencies. Data integration combines data from multiple sources to form a coherent data store. Data transformation routines segregate data into appropriate forms for mining. Data reduction can be used to obtain a reduced representation of the data while minimizing the loss of information content.

 

Why Data Pre-processing?

When exploring this wealth of information data pre-processing cleans and prepares the data before predictive models are developed. Predictions from incorrect data can be difficult to debug, or worse, can lead to inaccurate or misleading results that impact system performance and reliability. The goal here is to find the most predictive features of the data and filter it so it will enhance the predictive power of the analytics model. Some common techniques include feature selection to reduce high-dimension data, feature extraction and transformation for dimensionality reduction, and domain analysis such as signal, image, and video processing.

The information gathered from data pre-processing is then taken and implemented across a number of analytics-driven embedded systems. An example of this is the innovation in using Big Data and analytics to make cars smarter. Automotive OEMs are collecting enormous amounts of data from real-world driving situations (think millions of miles of driving), recording data such as engine performance, video, radar, and other signals. This data is used to generate important metrics such as fuel economy and performance at the fleet level. Engineering teams are also using this real-world data to design, develop, and test new types of automotive systems, such as advanced driver assistance systems (ADAS).

 

Benefits

In some implementations, analytics are performed in the cloud with the intent to improve the performance of existing embedded systems. For example, BuildingIQ is a leading provider of advanced energy management software and designs climate-control systems to reduce energy consumption in commercial buildings. The analytics incorporated into these systems include engineering data from power meters, thermometers, pressure sensors, and other HVAC sensors combined with business data from weather forecasts, real-time energy prices, and demand response data. The result is a cloud-based service that is able to adjust the building’s existing HVAC embedded systems and lower energy consumption by up to 25%.

In other cases, analytics are better run directly in an embedded system. For instance, a design team at Scania, the Swedish truck manufacturer, embeds analytics into their emergency braking systems to provide real-time crash avoidance to reduce accidents and meet stringent EU safety regulations. Engineering data from cameras and radar are processed in real time for object and road marking detection, which is subsequently fused to signal collision warning alerts and automatic brake requests.

As the Scania case shows, there’s a growing need to put more of the data pre-processing and data reduction on the sensor or embedded device itself in order to optimize speed and power. The accelerating Internet of Things trend towards smarter and more connected sensor networks is only adding to that pressure. This has the benefit of shrinking the amount of data that is transferred over the network, which reduces the cost of transmission and can lower the power consumption of wireless devices. For this reason, good system designers should conduct local preprocessing wherever they are able and only upload the useful information or predictive signal.

As the number of possible applications increase, we’ll be able to benefit from the impact of analytics-driven embedded systems across a wider variety of industries, where data pre-processing will continue to play an important step in how we shape our world and how we operate in it.

 

About the Author 

paul-pilotte-technical-marketing-manager-mathworksPaul Pilotte

Paul Pilotte has more than 20 years of experience in technical marketing and development in technical computing, security software, data communications, and test-equipment markets. He is currently  a Technical Marketing Manager at MathWorks focusing on MATLAB toolboxes for statistics, optimization, symbolic math, and computational finance. He holds a Bachelor’s and Master’s degrees in electrical engineering from MIT and an MBA from Babson College.

Got expertise in Business Intelligence  / Machine Learning / Big Data / Data Science? Showcase your knowledge and help Analytics Vidhya community by posting your blog.

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