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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?
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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 data mining can do for your company and Practical Uses of Data Mining in Businesses

Mrinal Singh
Mrinal Singh Last Updated : 01 Jul, 2021
5 min read

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

Data mining is a technique of extracting and finding patterns in massive data sets by linking practices at the intersection of machine learning, statistics, and database management systems. Today’s article is about what data mining can do for your company, and after that, we will look into some of the practical use cases of data mining in your company.

data mining 1
Photo by Carlos Muza from Unsplash 

Why Does Your Company Needs Data Mining

Many consulting firms in the industry can help your company better utilize their data, from capturing it to analyzing it consistently with industry standards and beyond what others currently do with their data.

There is a wealth of knowledge and expertise to be gained and gleaned from the data that your company could capture, and it is essential to learn how to utilize that data and make more informed business decisions.

What Should Companies Do?

Many consulting firms offer to consult services to help you understand what your data is saying to you and your company. It also helps with the planning and full-scale implementation of any new data program.

As data consultants, companies should focus on the complex and exciting problems that your data will lay bare for your company. There is often no other way to find out what the issues are or what the real cause of the problem is until you capture the data, drill down into it and organize it so that you can see the patterns and get a better understanding of where the problem is coming from.

This is likewise valid for organizations that do not spawn any issues but are not moving forward with new initiatives or growing at the required or desired rate. The data consultants in a company do this data mining, and then that data is analyzed to find those previously unknown patterns to your company.

Many new ideas are brought to the previously unseen or unknown surface with this form of data analysis. This is the power of data, massive data.

How Data Mining Benefits Your Company If Done In The Right Way

 

How Data Mining Benefits Your Company If Done In The Right Way
Photo by Franki Chamaki from Unsplash

If you have data coming in that is not being captured and stored for that analysis, it needs to be charged. Data mining experts can work for you to have a system to capture your data and then ensure that you can review and analyze the data to make sense around the company and to the top leadership.

Information is only practical if learned, and experts cannot make decisions until it is clear and valuable. This is where the consultants and their data mining come in, and this is why you would hire data mining experts to perform data mining for you.

Once you ought the method set up, you can enter the info you want to see, and the system will provide it for you. The data will be there, fully captured and organized, tagged, and fully ready for analysis. It just takes a trained consultant to show you what the data is saying and teach you how best to use those dates for your company’s future decisions.

Practical Uses of Data Mining in Businesses

Data mining is not something new. Over time people have been gathering information to use for whatever purposes it suits them. Both sides will always try to find out just how large the other force is in times of war.

This way, they will be able to make the necessary adjustments or strategies. Unprocessed data also referred to as raw data, are, in fact, meaningless. It is only when the data is processed that it starts to have meaning. Data mining is defined as a way of converting raw data to something useful.

In the past, data analysis was limited to a group of people who had the necessary skills needed to interpret it. Today, however, there are now many different companies that offer analytics consulting or data analysis consulting. It is essential to know that there is a difference between data analysis and data analytics.

Know The Difference Between Data Analysis and Data Analytics

Data analysis is when you gather data from different sources then make it into something useful. Data analytics, meanwhile, is a platform wherein you use other models on the data to get additional insights.

Still, confused about the difference between the two? Let’s have a simple example. Suppose a convenience store decides to look at its sales on Fridays. Through data mining, it finds out that beer sales increase during Fridays. Data analysis consulting would likely tell you what percentage of the Friday sales is for beer and what type of beer customers prefer. Undergoing analytics consulting, meanwhile, would include information like what time during Fridays do customers buy beer.

By using these different sets of data, the owner of the convenience store can do several things. First, the owner can ensure that there is an ample supply of beer during Friday nights. Alternatively, the owner can also decide to offer discounts on other days, knowing full well that sales are relatively low during other days. Regardless of what decision the owner makes, the end goal is always using the information to increase profits.

So what did the owner do?

First, the owner did some data mining by getting figures for the number of beers sold on Fridays and the types of beer sold. Conducting data analysis consulting these results will give the owner a picture of how his store is faring on Friday nights.

Yet, the owner does not stop there and does analytics consulting new additional factors like time of sale. This, in turn, will give the owner a clearer picture, and the owner will be able to act accordingly, ensuring an increase in sales.

Last Point To Notice

Remember that everything, the raw data mainly, has always been available to the owner. The owner simply needed to find a way to make sense of everything before taking any new action. This is the primary purpose why corporations should adopt this and grasp this in mind. Data mining indeed can help enterprises to but only when companies know how to use whatever was gathered and apply the knowledge gained accordingly; otherwise, it will just be meaningless numbers.

If you enjoy reading this article, I am sure that we share similar interests and are/will be in similar industries. So let’s connect via LinkedIn and Github. Please do not hesitate to send a contact request!​_

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

 

blogathondata mining
Mrinal Singh
Mrinal Singh

Data Scientist and a Technical Writer! I will give you the best of Open-Source and AI.

Talks about #chatgpt, #opensource, #contentcreation, #communitybuilding, and #artificialintelligence

Technical Writer | Data Science, ML, AI, Open-Source | Do More with Data - Litmus

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