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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

What I did when I had to work with unstructured data?

Palak
Palak Last Updated : 14 Dec, 2020
4 min read

This article was published as a part of the Data Science Blogathon.

Introduction

I am sure those of you working with data in any capacity has been faced with the problem of working with bad data, inconsistent data, unclean data, call it as you may. For those working afar from data, as per a report by Forbes, 60% of any data professional’s time is spent in cleaning and organizing unstructured data. Yes, that’s a lot of time invested but in my opinion, is the foundation to derive results.

Here I curate 7 instances from my personal experience of working with unstructured data for almost 3 years now. I hope many of you reading can relate with at least a few.

 

1. Absence of Unique Identifier

To join two or more tables, normally there exists a common column, variable, parameter, etc that can be used to join the tables. Here I was faced with the problem of the absence of a unique identifier or we may say the absence of a primary and foreign key. Which meant trouble, more specifically while reporting on parameters across tables.

To get ahead of the problem, we created our key by concatenating values from certain columns and thus, building a table with columns of interest on that. Sounds chaotic? yes; Got us results? also yes.

 

2. Different nomenclature

While working with unstructured geographical data, I came across different spellings of a single geographical jurisdiction. For example, Chhattisgarh (a state in India) was spelled as Chattisgarh, Chhatisgarh, and Chhattisgarh. Which again meant trouble.

To tackle the problem, we created an interim layer of mapping, allocating codes to the string value and thus fetching the correct spelling from the master table via codes on the main data to get going.

Although in my opinion building systems that restrict user’s choice to spell the geography and instead of providing them with a pre-populated drop-down list would mean wiping the problem altogether.

 

3.Collating data from different file formats

unstructured data - collating
Image from Johnstone High School

I have been faced with this problem for almost half of my total tenure of working with data. I had to work with data bifurcated into different tables, all with different formats. For example, one was an SQL file and the other came from an xlsx file.

There wasn’t much to do here, but to be certain of not missing out on all parts of a whole, i.e. collating the sub-divided data.

We can’t control everything, sometimes it is about working around.

 

4. Linguistic barrier

This was one interesting instance, where linguistic and dialect diversity of the Indian subcontinent had a direct impact on data consistency. For example, Thiruvananthapuram, a city in the state of Kerala also spelled as Tiruvanantapuram made my days more anxious than usual. While working on geographical jurisdiction data of District and further Town level for urban areas and Village level for rural areas, posed a big challenge especially to reconcile the data.

The problem was dealt with in more than one way, fuzzy logic spelling checker being the stepping stone which we sought out to maintain a scoring system to reconcile the data on the proposed correct spellings, solving close to 60% of the problem. While the rest 40% was dealt with manually.

 

5. The most common, missing data

In my very recent experience, I faced the missing data problem for various data types and sizes.

We were using a key-value pair system to get away from problems defined earlier like the different nomenclature and/or linguistic barrier, etc. It was when we found out that at least 35% of values were missing, that I had to come up with a pattern recognition algorithm to impute the missing values while maintaining the sanctity of the very mechanism, i.e. keeping it unique.

While in other problems of the same nature we referred back to the Data Engineering team to rebuilt data pipelines more than once using various sources of data to come up with a Single Source of Truth.

 

6. Not so flawless data architecture

Unstructured Data - Not so Flawless architecture
Image from The Data Modelling Hub

In my opinion, if the data collection, flow, and usage are not standardized it leads to delays in getting relevant, useful data. This has got me to write about two instances that occurred because of the same problem.

First, Imagine getting data points meant to be collected in column ‘x’ from column ‘y’. Now it was a mammoth activity to make architectural changes, so instead, we made optimized data models that were automated to update every time a new batch of data came in.

Second, Not receiving any data for one column with the sword of delivery hanging on heads. This meant a work-around to meet the deadline, we sought out to do the Math and used the Derivative logic to get the value for the missing column. Although, we later received the value for the column which was what we derived.

 

7. Different date formats

Honestly, as I was writing the header I cringed

Referencing point 3 here, since the data came in different file formats it was no surprise that the column formats for one variable were different as well.

There wasn’t much we could do but to cast and/or convert while collating the data. However, while using visualization tools the problem did not seem as grave and was only a matter of few clicks to make it relevant and compatible to use.

 

End Notes

In conclusion, I believe cleaning and organizing the unstructured data is essential to deliver quality results. I hope these excerpts provided relatable or insightful instances of a few amongst the many problems that exist in the real world.

blogathon
Palak
Palak

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