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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

The Key Concepts To Investigating Your Dataset

Vamshi krishna
Vamshi krishna Last Updated : 07 Dec, 2020
5 min read

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

“Don`t jump into modeling. First, understand and explore your data!”

Overview

This is common advice for many data scientists. If your data set is messy, building models will not help you to solve your problem. What will happen is “garbage in, garbage out.” In order to build a powerful machine learning algorithm. We need to explore and understand our data set before we define a predictive task and solve it.

Data analysis is a nuanced discipline, and there are enough ways to slice and dice data to make a beginner’s head spin. A common question data analysts hear is “where do I even start with my analysis?” These are some hints to help start you on the right path thinking about a methodical way to uncover the answers in your data.

 

Introduction

Before going further, Data scientists spend most of their time exploring, cleaning, and preparing their data for modeling. This helps them to build accurate models and check assumptions required for fitting models.

Create meaningful data visualizations, predict future trends from the data.

If you are good at understanding data preparation almost 80% of the work is completed.

 

Table of Contents

  • Ask the right questions?
  • Analyze different subsets of data
  • Explore trends
  • Find your blind spots
  • Investigate the whys

Ask the right questions

Whether it’s surveying results, sales data, or an email campaign, you’ve collected data for a specific purpose. By extension, apply this purpose to the questions you’re asking of the data itself. Beginning with some specific questions can keep your research focused and allow you to see the forest through the trees. A question like “what does my revenue look like for the past 3 years” is vague and allows for exploration but also confusion.

Instead, something like “which channel brings in the most revenue for the past 3 years” has a clearer answer. Subsequent questions may be: “which department brings in the most revenue per year” or “are sales in climbing gear increasing or decreasing this year?” It’s important to have a specific question in mind when you begin data analysis so as to provide some structure and avoid stumbling into false positives.

investigate data - channel revenue

Analyze different subset of data:

It’s easier to spot relationships if you analyze the data from different subsets. For example, segment your revenue data by channel like the chart above, or by the department. Experiment with the subsets and variables that make the most sense of the questions you developed in the previous step.

This design focuses on allowing you to stay within your train of thought and smoothly transition from question to question, without tripping up on formatting or equations. It can also be helpful to use what would be referred to as a pivot table in Excel. In our outdoor gear retailer example, you can switch from a quarterly view to revenue by a quarter of the year just by selecting in a drop-down menu. The graph below then is an aggregate of each quarter’s revenue between 2010 and 2013.

investigate data - quarterly revenue

Explore trends

Experiment with your time variables. Look at the quarter, month, or week, whichever makes sense based on what you’re looking for. Sometimes what is missing is also just as important as what is there. If there are holes in your data analysis, take note. It can be helpful to take notes through your analysis, reminders of what you’d like to research or discuss with colleagues later.

Take a look at this quarterly analysis of revenue by the department. It’s not very helpful because it’s hard to spot trends.

Investigate data - quarterly revenue

This yearly line graph makes it much easier to see that Climbing is the fastest-growing department and Running sales have been decreasing for the past three years.

yearly department revenue

 

Find your blind spots

Do you bump up against a particular question regularly? There is a fine line between collecting as much data as you can get answers, and frustrating your users with too many questions. Weigh this consideration when deciding how much data you’d like to collect. Then you can either find a way to gather that information from your users or at least write it on a data collection wish list for later discussion.

  • Actually been collected
    for the task you are being asked to do. And you are being asked to make the
    data validate an outcome that has already been decided.
  • Most organizations don’t think scientifically. They don’t create a hypothesis and then decide what data they need to collect to validate it. They choose an outcome, then make the data fit.
  • Often the data come from something else entirely – often as a byproduct of a business process. Then someone has the bright idea “We could use this to work out”

  • Analyzing the below graph, the graph illustrates the information about the blind spots of a data set. Hidden data will be one of the drawbacks to getting a solution. Overall, finding outliers will be a solution.

  • Outlier correction based on the R parameter. The leftmost graph shows the original data with detected outliers. The middle graph uses a noise value of zero to place, or correct, the location of the outliers in the linear model. The rightmost graph places the outlier near the linear model at a distance based on a positive value for R (R = 0.5). 
  • blind spot data investigate

Investigate the whys:

After your daily, weekly, or quarterly analysis, take your charts, notes, and conclusions to the rest of the team and start trying to piece together as much as you can. The data can tell you what is happening, but not the why. The why requires piecing together the backstory. Because so many factors play into your sales data, coming together with your team to discuss insights from your data can lead to a lot more understanding. The marketing manager may know something about the third quarter’s climbing gear sales that the business analyst didn’t.

 

why

 

Data analysis is a continual process and the best way to approach it is to try to get less and less wrong. You probably won’t ever have all the data you want or need to answer every question about your business, but you can at least push toward more answers and better decisions. This continual feedback loop (question, analyze, investigate, repeat) can be improved but will never be perfect.

 

Endnotes

Understanding and interpreting data are a very crucial step in machine learning. In this blog post, we tried to provide an overview of techniques that can help you to better know your data

Depending on the size, dimension, and type of your data, you can choose the algorithm. For instance, when you have big raw data, you can use representative examples instead of random samples. If you have a wide data set, you can also find the important dimensions to understand the representative samples.

Different techniques can give you different insights on your data. It is your job to use the tools to solve the mystery like a detective.

Vamshi krishna
Vamshi krishna

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