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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

5 Simple manipulations to extract maximum information out of your data

Kunal Jain
Kunal Jain Last Updated : 28 Apr, 2015
3 min read
How would you distinguish between best, good and worst analyst from a group of analysts?
I would simply provide them same problem and data set and ask them to create a predictive model out of it.
Why is this a sure shot test?
This is the core of any analysis or any data mining activity. The degree to which an analyst is able to bring out insights and trends is what differentiates a good analyst from a bad one. This is the sole idea on which competitions such as the ones running on Kaggle are built. In larger scheme of things, it is this ability to extract information from data, which differentiates companies like Google, Amazon and Capital One from their competitors.
[stextbox id=”section”]Steps in creating competitive advantage while building predictive models: [/stextbox]
There are 2 key steps involved every time you try and create this competitive advantage:
  1. Cleansing and transforming datasets to create a comprehensive data set which captures all possible hypothesis
  2. Choosing the right set of predictive modeling tools and techniques which work on the data set created in last step and bring out insights
Both the steps in this process are equally important. We’ll focus on the first step in this article and discuss step 2 at a later date.
Building a comprehensive data set takes time and effort. The more time you can spend in transforming variables and extracting information, the better your model would be expected to perform. You can not and should not rely entirely on your tools to identify trends, insights or relationships.
dataset creation
 
This article addresses a situation which we face on a daily basis as analysts. How do we extract maximum information from a defined data set? By no means, this is an exhaustive list of extracting every possible information. But, it is good enough to get you thinking on the right track. If you have any other thoughts / practices which you use to extract more information about the variables, please add them in comments
[stextbox id=”section”]1. Create variables for difference in date, time and addresses[/stextbox]
While you might be using date and time values on their own, you can create new variables by considering differences in dates and time. Here is an example hypothesis: An applicant who takes days to fill in an application form is likely to be less interested / motivated in the product compared to some one who fills in the same application with in 30 minutes. Similarly, for a bank, time elapsed between dispatch of login details for Online portal and customer logging in might show customers’ willingness to use Online portal.
Another example is that a customer living closer to a bank branch is more likely to have a higher engagement than a customer living far off.
[stextbox id=”section”]2. Create new ratios and proportions[/stextbox]
Instead of just keeping past inputs and outputs in your dataset, creating ratios out of them might add a lot of value. Some of the ratios, I have used in past are: Input / Output (past performance), productivity, efficiency and percentages. For example, in order to predict future performance of credit card sales of a branch, ratios like credit card sales / Sales person or Credit card Sales / Marketing spend would be more powerful than just using absolute number of card sold in the branch
[stextbox id=”section”]3. Apply standard transformations[/stextbox]
By looking at variations and plots of a variable along with output, you can see if applying basic transformations on variables creates a better relationship. Most commonly used transformations include Log, exponential, quadratic and trignometric variations. For example, Log(Marketing spend) might have a more representable relationship with Sales as compared to absolute Marketing spend.
[stextbox id=”section”]4. Include effect of influencer[/stextbox]
Influencer can impact on the behaviour of your study significantly. Influencer could be of various form and sizes. It could be an employee of your Organization, agent of your Organization or a customer of your Organization. Bringing the impact of these related entities can improve the models significantly. For example, a loan initiated by a sub-set of brokers (and not all brokers) might be more likely to be transferred to a different entity after the lock-in period. Similarly, there might be a sub set of Sales personnel involved who do a higher cross-sell to their customers.
[stextbox id=”section”]5. Check variables for seasonality and create the model for right period[/stextbox]
A lot of businesses face some kind of seasonality. It could be driven by tax benefits, festive season or weather. If this is the case, you need to make sure that the data and variables are chosen for the right period. This article can give you more details on how to tackle effects of seasonality while modeling.
What do you think of these manipulations? Are there some other transformations which you use while creating a predictive model? In case there are, please add them in the comments below

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AnalyticsBusiness Analyticsdata manipulationdata miningdata transformation
Kunal Jain
Kunal Jain

Kunal Jain is the Founder and CEO of Analytics Vidhya, one of the world's leading communities of Al professionals. With over 17 years of experience in the field, Kunal has been instrumental in shaping the global Al landscape. His expertise spans diverse markets, from developed economies like the UK to emerging ones like India, where he has successfully led and delivered complex data-driven solutions. As a recognized thought leader, Kunal has empowered countless individuals to realize their Al ambitions through his visionary approach to Al education and community building. Before founding Analytics Vidhya, Kunal earned both his undergraduate and postgraduate degrees from IIT Bombay and held key roles at Capital One and Aviva Life Insurance across multiple geographies. His passion lies at the intersection of analytics, Al, and fostering a thriving community of data science professionals.

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Responses From Readers

Clear

Kumar
Kumar

Hi Kunal, Is it possible to give a single link for all related articles. I am following AV for sometime and got to this article only thru the link provided by Sunil's article on variable creation. The recommended article section (above ) for this article includes none of those "Data Cleaning & transformation" articles by Sunil - which are very relevant to this one (though they are published later ) Aggregating all related articles under one header and doing it as a ongoing activity - would be very helpful for followers

123

Dheeraj Singh
Dheeraj Singh

Hi Kunal, Could you please provide article on Outlier/Anamoly detection with python library like Isolation random forest , one class svm, LOF.

123

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