Data Mining Fundamentals For Beginners

Explore the knowledge discovery in databases (KDD) framework for data mining, a nine-step, iterative process that turns raw data and business goals into actionable insights.

Explore the SEMMA data mining process, including sampling, exploration, modification, modeling, and assessment, and its cyclic path from data selection to deployment, with clustering and predictive modeling examples.

Explore CRISP-DM, the data mining process not dependent on industry or toolsets, with six phases—business understanding, data understanding, data preparation, modeling, evaluation, deployment—forming a cyclic lifecycle.

Compare DMA and Cryptome, outlining common, divergent, and missing steps across data mining processes, from business understanding to deployment, and note how modeling and evaluation fit in.

Explore TDSP, the Team Data Science Process, an agile framework from Microsoft that ties business understanding, data acquisition and understanding, modeling, deployment, and customer acceptance into a collaborative lifecycle.

Explore the four R Studio areas, write and run code, plot results, and manage files. Learn to navigate directories, use the terminal, and access packages.

Launch Jupyter notebooks via Anaconda, create and run notebooks, add markdown cells, save as .ipynb in a directory, and use shortcuts to edit and navigate cells.

Explore the Rapid Miner Studio interface, navigate the central design area, and manage data in local repositories, importing data and previewing results while building a decision tree workflow.

Explore data reduction to simplify datasets by removing unused columns and focusing on meaningful variables, using PCA concepts and iris data as examples.

Explore principal component analysis in r with the iris dataset: prepare numeric data, run prcomp with scaling, and visualize components to distinguish species.

Demonstrate data reduction in Python using PCA on the iris dataset, reducing 150 samples with four features to two components via fit and transform, with a final scatter plot.

Import the Iris data, select attributes, and apply principal component analysis in RapidMiner to keep about 95 percent variance with two components. Visualize scatter matrix to show class separation.

Explore clustering by grouping similar data objects using models such as connectivity, distribution, density, and centroid approaches, including k-means.

Explore k-means clustering in R using the iris dataset, remove non-numeric columns, compute three clusters, inspect cluster assignments and sizes, compare with species, and visualize results with color-coded plots.

Learn how to apply k-means clustering in Python using scikit-learn on the iris dataset, from loading data to fitting the model, analyzing labels and centers, and visualizing clusters with matplotlib.

Demonstrates clustering with RapidMiner by importing the dataset, selecting numeric attributes, configuring k-means with three clusters, running the model, and saving it for future use.

Explore classification, assigning new observations to predefined classes from a training set, with spam filters as examples, and compare supervised versus unsupervised learning and key algorithms.

Learn to build a wine type classifier in R by normalizing features, splitting data into 70/30 training and testing sets, and applying the kanon function for predictions.

Perform classification in Python using a k-nearest neighbors style approach. Load data with pandas, scale features with StandardScaler, split with train_test_split, fit with a k, and evaluate accuracy with accuracy_score.

In rapid miner, import the wine dataset, set the label and split the data 70/30. Apply the key means model and evaluate classification performance with accuracy and confidence.

Explore anomalies and how to detect them in data. Identify univariate, bivariate, and multivariate outliers and learn the interquartile range method with Q1, Q3, and 1.5 times the interquartile range.

Identify outliers by computing Q3 and Q1 with summary, calculate IQR, set upper and lower bounds, filter data beyond bounds, and verify with a box plot in R.

Explore anomaly detection in Python by identifying and removing outliers in the iris dataset with pandas and matplotlib, using Q1, Q3, and IQR to set upper and lower bounds.

Explore anomaly detection in RapidMiner using the Titanic dataset, selecting a subset of attributes and applying a distance-based outlier rule with Euclidean distance.

Explore association analysis with grocery data by identifying frequent itemsets and rules, calculating support and confidence to predict what customers may buy next, and learn apriori and fp-growth algorithms.

Explore association analysis in R using the groceries dataset and the apriori algorithm, generating and inspecting rules with support and confidence while visualizing results through 3d plots and graph layouts.

learn to perform association analysis in python using the groceries dataset, converting transactions to a boolean matrix, running apriori, generating and filtering association rules by support, confidence, and lift.

Explore association analysis in RapidMiner using a groceries dataset, converting data to binomial, applying FP-growth, and generating association rules with support, confidence, and lift.

Learn regression analysis, linking a dependent variable with predictors, illustrated by wine quality. Explore linear and logistic regression and regularization like ridge regression and stepwise regression to prevent overfitting.

Load the wine quality dataset, perform recursive feature elimination to select density, chlorides, volatile acidity, sulfates, and pits, and compare stepwise, lars, forward stagewise, and lasso models in R.

Apply regression analysis in Python using lasso and SVR on the wine quality dataset, with train-test split and feature standardization, and assess accuracy via mean squared error.

Apply regression analysis in RapidMiner to predict wine quality and concrete data using linear regression, feature selection, and RMSE-based evaluation.

Explore sequence mining, where order matters, unlike association analysis, with applications in genetic sequencing, medical diagnosis, and recommendations. Learn algorithms such as GSP, speed, prefix span, and hidden Markov models.

Explore sequence mining in R with hidden Markov models using speed package. Compare 1-, 2-, and 3-state models with Bayesian information criterion to select fit for reaction time and accuracy.

Explore sequence mining in Python using a hidden micro model with the Etchingham Learn package, including data loading, preprocessing, model fitting, prediction, and visualization of reaction-time accuracy patterns.

Explore sequence mining in RapidMiner using the GSP algorithm to discover sequential patterns from transaction data, setting relevant columns, the time attribute, and parameters like minimum support and max gap.