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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

The 6 Steps of Predictive Analytics

Yamini
Yamini Last Updated : 12 Oct, 2022
5 min read

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

Introduction

With technological evolution, data dependence is increasing much faster. Gone are the days when business decisions were primarily based on gut feeling or intuition. Organizations are now employing data-driven approaches all over the world. One of the most widely used data applications is ‘Predictive Analytics’. Predictive analytics is widely used for solving real-time problems, be it forecasting the weather of a place or predicting the future scope of a business.

Definition of Predictive Analytics

“Predictive Analytics refers to the field that applies various quantitative methods on data to make real-time predictions.”

It provides a method of approaching and solving problems using various technologies, essentially machine learning. Predictive Analytics often makes use of machine learning algorithms and techniques to build models that make predictions.

Tools needed to begin: Python programming, Numpy, Pandas, Matplolib, Seaborn, Scikit-Learn

Predictive Analytics: Steps

Predictive Analytics

Figure 1: The Predictive Analytics Process

(1) Problem Understanding and Definition:

This is the initial stage in the process of predictive analysis. This is a vital stage because we first need to understand what exactly the problem is to frame the solution. When a stakeholder approaches you with a certain problem, the first step would be to know the stakeholders’ requirements, the utilities available, the deliverables and finally, know how the solution looks from the business perspective.

Sometimes the requirements of the stakeholders may not be clearly defined. It becomes our responsibility to understand precisely what is to be predicted and whether the outcome solves the defined problem. The dynamics of the solution and the outcome completely change based on the problem definition.

Converting a business problem into an analytical one is the most important part of predictive analysis. Hence explicitly define what is to be predicted and how does the outcome look like.

(2) Data Collection:

This is the most time-consuming stage. Sometimes, the required data may be provided by the stakeholder, from an external database or in some cases, you may have to extract the data. It is possible that the data so collected may not be sufficient for framing the solution. You may have to collect data from many sources. Think about how much access you have to the dataset that is required.

Since the outcome of the predictive model relies entirely on the data used, it is important to gather the most relevant data that aligns with the problem requirements. Here are a few things to be kept in mind while searching for a dataset:

  • Format of the data
  • Period across which the data is collected
  • The attributes of the dataset
  • Does the dataset meet your requirements?

(3) Exploratory Data Analysis:

Once you have the dataset ready, you now may be willing to build your predictive model. But before we start, it is crucial to know the properties of your data. Understanding the kind of data you have, the features it possesses, the target or outcome variable, and the correlation among these features all play a role in designing a suitable model. The main aim of EDA is to understand the data. This may be achieved by answering the below few questions:

  • What are the data types present in the dataset?
  • What is the dimensionality of the dataset?
  • What does the data distribution look like?
  • Is there any missing data?
  • Is there any prominent pattern in the data distribution?
  • Do you observe outliers?
  • How are data features correlated to each other?
  • Does their correlation affect the outcome?

Sometimes the data collected contains a lot of redundant data. If such data is fed as input to the model, there is a high possibility that the model makes wrong predictions. Hence it is important to perform EDA on the data to ensure that all the outliers, null values and other unnecessary elements are identified and treated. Identifying the patterns in the data makes it easier to decide the model’s parameters. EDA helps us improve the model’s accuracy even before it is built.

EDA generally has two components- numerical calculations and data visualizations. Calculating Standard Deviation, Z-score, Inter-Quartile Range, Mean, Median, Mode, and identifying the skewness in the data are some ways of understanding the dispersion of data across the dataset. Graphical representations such as heat maps, scatter plots, bar graphs, and box plots help get a wider view of the dataset.

(4) Model Building:

After applying EDA, it is finally time to build predictive models using machine learning. In the dataset, we use the predictor variables to make predictions on the target variable.

Target: The dependent variable whose values are to be predicted.

Predictors: The independent attributes in the dataset that are used to predict the value of the target variable. Once the target is identified, all other columns become the predictor variables.

Model Building | Predictive Analytics

                                            Figure 2: Model Building

Here we consider the model a calculator that takes in inputs and gives out the predicted output. We may have to build a Regression or a Classification model based on the problem.

Regression algorithms such as Simple Linear Regression, Multi Linear Regression, Decision Tree Regression etc., may be used to get desired results. Such models are used when the target is a numeric feature.

Example: Predicting the house prices

While classification models are used when the target is a categorical feature, the classification problems may be a binary classification or multiclass classification.

Binary classification: The target has only two possible categories.

Multiclass classification: The target has more than two possible outcome categories.

Apart from these, unsupervised learning algorithms such as Clustering and Association algorithms can also be used based on the requirement.

(5) Model Evaluation:

Once the model is built, the next stage would be to analyze the performance of the model. Evaluating the model based on different scenarios and parameters thereby contributes to deciding ‘the most effective’ model for solving the given problem. Usually, one or more metrics are used to know how good the model performs.

Different measures are used for rating the performance of machine learning models.

For regression models: Mean Squared Error (MSE), Root Mean Squared Error (RMSE), R Squared (R2 Score)

For classification models: F2 Score, Confusion Matrix, Precision, Recall, AUC-ROC

(6) Model Deployment:

Now that the model has been built, tested and evaluated, it is time to deliver it to the stakeholder. Model deployment involves placing the model into a real-world application that can be used for its intended purpose. This may be done by using the model in a software application, integrating it into a hardware device, building a framework around the model or using the model itself as a ‘data product’.

Conclusion

This article taught us the primary steps of predictive analytics. One needs to be aware of these while dealing with predictive analytics problems.

  • Framing and understanding the problem statement
  • Collecting and preparing the data
  • Build appropriate models
  • Evaluate them to choose the best one
  • Deploy them in the required format

However, these were just an overview of the most important stages; further steps can also be performed depending on the problem.

Now that you have a basic understanding of how prediction models are designed, you can start learning the tools and concepts required to build your first prediction model.

The media shown in this article is not owned by Analytics Vidhya and is used at the Author’s discretion.

Yamini
Yamini

Data science enthusiast and storyteller. Sharing my learnings and findings from the world of NLP, data science and machine learning through my articles. Let's explore the world of data together! Spreading knowledge, one post at a time.

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Responses From Readers

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Sai mohan ayinam
Sai mohan ayinam

That's a great blog about predictive analysis and hopefully we can see more blogs near future

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