Decision tree is a type of supervised learning algorithm (having a pre-defined target variable) that is mostly used in classification problems. It is a tree in which each branch node represents a choice between a number of alternatives, and each leaf node represents a decision.
Data preprocessing techniques
See my Paris applied psychology conference paper here
https://www.slideshare.net/jasonrodrigues/paris-conference-on-applied-psychology
or
https://prezi.com/view/KBP8JnekVH9LkLOiKY3w/
This document discusses and provides examples of supervised and unsupervised learning. Supervised learning involves using labeled training data to learn relationships between inputs and outputs and make predictions. An example is using data on patients' attributes to predict the likelihood of a heart attack. Unsupervised learning involves discovering hidden patterns in unlabeled data by grouping or clustering items with similar attributes, like grouping fruits by color without labels. The goal of supervised learning is to build models that can make predictions when new examples are presented.
This document discusses various machine learning techniques for classification and prediction. It covers decision tree induction, tree pruning, Bayesian classification, Bayesian belief networks, backpropagation, association rule mining, and ensemble methods like bagging and boosting. Classification involves predicting categorical labels while prediction predicts continuous values. Key steps for preparing data include cleaning, transformation, and comparing different methods based on accuracy, speed, robustness, scalability, and interpretability.
PCA and LDA are dimensionality reduction techniques. PCA transforms variables into uncorrelated principal components while maximizing variance. It is unsupervised. LDA finds axes that maximize separation between classes while minimizing within-class variance. It is supervised and finds axes that separate classes well. The document provides mathematical explanations of how PCA and LDA work including calculating covariance matrices, eigenvalues, eigenvectors, and transformations.
This presentation gives the idea about Data Preprocessing in the field of Data Mining. Images, examples and other things are adopted from "Data Mining Concepts and Techniques by Jiawei Han, Micheline Kamber and Jian Pei "
1. Machine learning involves developing algorithms that can learn from data and improve their performance over time without being explicitly programmed. 2. Neural networks are a type of machine learning algorithm inspired by the human brain that can perform both supervised and unsupervised learning tasks. 3. Supervised learning involves using labeled training data to infer a function that maps inputs to outputs, while unsupervised learning involves discovering hidden patterns in unlabeled data through techniques like clustering.
The document provides an overview of data mining concepts and techniques. It introduces data mining, describing it as the process of discovering interesting patterns or knowledge from large amounts of data. It discusses why data mining is necessary due to the explosive growth of data and how it relates to other fields like machine learning, statistics, and database technology. Additionally, it covers different types of data that can be mined, functionalities of data mining like classification and prediction, and classifications of data mining systems.
Classification techniques in data miningKamal Acharya
The document discusses classification algorithms in machine learning. It provides an overview of various classification algorithms including decision tree classifiers, rule-based classifiers, nearest neighbor classifiers, Bayesian classifiers, and artificial neural network classifiers. It then describes the supervised learning process for classification, which involves using a training set to construct a classification model and then applying the model to a test set to classify new data. Finally, it provides a detailed example of how a decision tree classifier is constructed from a training dataset and how it can be used to classify data in the test set.
This document discusses association rule mining. Association rule mining finds frequent patterns, associations, correlations, or causal structures among items in transaction databases. The Apriori algorithm is commonly used to find frequent itemsets and generate association rules. It works by iteratively joining frequent itemsets from the previous pass to generate candidates, and then pruning the candidates that have infrequent subsets. Various techniques can improve the efficiency of Apriori, such as hashing to count itemsets and pruning transactions that don't contain frequent itemsets. Alternative approaches like FP-growth compress the database into a tree structure to avoid costly scans and candidate generation. The document also discusses mining multilevel, multidimensional, and quantitative association rules.
Visual data mining combines traditional data mining methods with information visualization techniques to explore large datasets. There are three levels of integration between visualization and automated mining methods - no/limited integration, loose integration where methods are applied sequentially, and full integration where methods are applied in parallel. Different visualization methods exist for univariate, bivariate and multivariate data based on the type and dimensions of the data. The document describes frameworks and algorithms for visual data mining, including developing new algorithms interactively through a visual interface. It also summarizes a document on using data mining and visualization techniques for selective visualization of large spatial datasets.
Ensemble Learning is a technique that creates multiple models and then combines them to produce improved results.
Ensemble learning usually produces more accurate solutions than a single model would.
This document discusses various data reduction techniques including dimensionality reduction through attribute subset selection, numerosity reduction using parametric and non-parametric methods like data cube aggregation, and data compression. It describes how attribute subset selection works to find a minimum set of relevant attributes to make patterns easier to detect. Methods for attribute subset selection include forward selection, backward elimination, and bi-directional selection. Decision trees can also help identify relevant attributes. Data cube aggregation stores multidimensional summarized data to provide fast access to precomputed information.
K-means clustering is an algorithm that groups data points into k number of clusters based on their similarity. It works by randomly selecting k data points as initial cluster centroids and then assigning each remaining point to the closest centroid. It then recalculates the centroids and reassigns points in an iterative process until centroids stabilize. While efficient, k-means clustering has weaknesses in that it requires specifying k, can get stuck in local optima, and is not suitable for non-convex shaped clusters or noisy data.
The document presents a machine learning presentation by five students. It discusses key machine learning concepts including supervised learning (classification and regression), unsupervised learning (clustering and association), semi-supervised learning, and reinforcement learning. Examples of applications are provided. The differences between traditional computer science programs and machine learning programs are outlined. The future of machine learning is predicted to include its integration into all AI systems, machine learning-as-a-service, continuously learning connected systems, and hardware enhancements to support machine learning capabilities.
Clustering is an unsupervised learning technique used to group unlabeled data points together based on similarities. It aims to maximize similarity within clusters and minimize similarity between clusters. There are several clustering methods including partitioning, hierarchical, density-based, grid-based, and model-based. Clustering has many applications such as pattern recognition, image processing, market research, and bioinformatics. It is useful for extracting hidden patterns from large, complex datasets.
Clustering is the process of grouping similar objects together. It allows data to be analyzed and summarized. There are several methods of clustering including partitioning, hierarchical, density-based, grid-based, and model-based. Hierarchical clustering methods are either agglomerative (bottom-up) or divisive (top-down). Density-based methods like DBSCAN and OPTICS identify clusters based on density. Grid-based methods impose grids on data to find dense regions. Model-based clustering uses models like expectation-maximization. High-dimensional data can be clustered using subspace or dimension-reduction methods. Constraint-based clustering allows users to specify preferences.
This document discusses machine learning concepts like supervised and unsupervised learning. It explains that supervised learning uses known inputs and outputs to learn rules while unsupervised learning deals with unknown inputs and outputs. Classification and regression are described as types of supervised learning problems. Classification involves categorizing data into classes while regression predicts continuous, real-valued outputs. Examples of classification and regression problems are provided. Classification models like heuristic, separation, regression and probabilistic models are also mentioned. The document encourages learning more about classification algorithms in upcoming videos.
Data pre-processing involves cleaning raw data by filling in missing values, removing noise, and resolving inconsistencies. It also includes integrating, transforming, and reducing data through techniques like normalization, aggregation, dimensionality reduction, and discretization. The goal of data pre-processing is to convert raw data into a clean, organized format suitable for modeling and analysis tasks like data mining and machine learning.
The document discusses the key steps involved in data pre-processing for machine learning:
1. Data cleaning involves removing noise from data by handling missing values, smoothing outliers, and resolving inconsistencies.
2. Data transformation strategies include data aggregation, feature scaling, normalization, and feature selection to prepare the data for analysis.
3. Data reduction techniques like dimensionality reduction and sampling are used to reduce large datasets size by removing redundant features or clustering data while maintaining most of the information.
This document provides an overview of key concepts in data preprocessing for data science. It discusses why preprocessing is important due to issues with real-world data being dirty, incomplete, noisy or inconsistent. The major tasks covered are data cleaning (handling missing data, outliers, inconsistencies), data integration, transformation (normalization, aggregation), and reduction (discretization, dimensionality reduction). Clustering and regression techniques are also introduced for handling outliers and smoothing noisy data. The goal of preprocessing is to prepare raw data into a format suitable for analysis to obtain quality insights and predictions.
The document discusses various data preprocessing techniques including data cleaning, integration, transformation, reduction, and discretization. It covers why preprocessing is important to address dirty, noisy, inconsistent data. Major tasks involve data cleaning like handling missing values, outliers, inconsistent data. Data integration combines multiple sources. Data reduction techniques like dimensionality reduction and numerosity reduction help reduce data size. Feature scaling standardizes features to have mean 0 and variance 1.
Data extraction, cleanup & transformation tools 29.1.16Dhilsath Fathima
Data preprocessing is an important step in the data mining process that involves transforming raw data into an understandable format. It includes tasks like data cleaning, integration, transformation, and reduction. Data cleaning identifies outliers, handles missing data and resolves inconsistencies. Data integration combines data from multiple sources. Transformation includes normalization and aggregation. Reduction techniques like binning, clustering, and sampling reduce data volume while maintaining analytical quality. Dimensionality reduction selects a minimum set of important features.
This document discusses various techniques for data preprocessing including data cleaning, integration, transformation, reduction, discretization, and concept hierarchy generation. Data cleaning involves handling missing data, noisy data, and inconsistent data through techniques like filling in missing values, identifying outliers, and correcting errors. Data integration combines data from multiple sources and resolves issues like redundant or conflicting data. Data transformation techniques normalize data scales and construct new attributes. Data reduction methods like sampling, clustering, and histograms reduce data volume while maintaining analytical quality. Discretization converts continuous attributes to categorical bins. Concept hierarchies generalize data by grouping values into higher-level concepts.
Data preprocessing involves cleaning data by handling missing values and outliers, integrating multiple data sources, and transforming data through normalization, aggregation, and dimension reduction. The goals are to improve data quality, handle inconsistencies, and reduce data volume for analysis. Major tasks include data cleaning, integration, transformation, reduction through methods like feature selection, clustering, sampling and discretization of continuous variables. Preprocessing comprises the majority of work in data mining projects.
This document discusses module 3 on data pre-processing. It begins with an overview of data quality issues like accuracy, completeness, consistency and timeliness. The major tasks in data pre-processing are then summarized as data cleaning, integration, reduction, and transformation. Data cleaning techniques like handling missing values, noisy data and outliers are covered. The need for feature engineering and dimensionality reduction due to the curse of dimensionality is also highlighted. Finally, techniques for data integration, reduction and discretization are briefly introduced.
The document provides an overview of key concepts in data preprocessing including data cleaning, feature transformation, standardization and normalization. It discusses techniques such as handling missing values, binning noisy data, dimensionality reduction, discretizing continuous features, and different scaling methods like standardization, min-max scaling and robust scaling. Code examples are provided to demonstrate these preprocessing techniques on various datasets. Homework includes explaining z-score standardization and dimensionality reduction, and preprocessing the Titanic dataset through cleaning, standardization and normalization.
Data mining and data warehouse lab manual updatedYugal Kumar
This document describes experiments conducted for a Data Mining and Data Warehousing Lab course. Experiment 1 involves studying data pre-processing steps using a dataset. Experiment 2 involves implementing a decision tree classification algorithm in Java. Experiment 3 uses the WEKA tool to implement the ID3 decision tree algorithm on a bank dataset, generating and visualizing the decision tree model. The experiments aim to help students understand key concepts in data mining such as pre-processing, classification algorithms, and using tools like WEKA.
Data preprocessing is a technique used to prepare raw data for data mining by cleaning data, handling missing values, smoothing noisy data, and reducing data size. It involves techniques such as data cleaning, integration, transformation, and reduction. Data cleaning identifies and removes errors and inconsistencies. Data integration merges data from multiple sources. Data transformation operations like normalization prepare data for certain algorithms. Data reduction reduces data size through aggregation, attribute selection, and other techniques. Preprocessing resolves issues in raw data to improve data mining results.
Data science involves extracting knowledge and insights from structured, semi-structured, and unstructured data using scientific processes. It encompasses more than just data analysis. The data value chain describes the process of acquiring data and transforming it into useful information and insights. It involves data acquisition, analysis, curation, storage, and usage. There are three main types of data: structured data that follows a predefined model like databases, semi-structured data with some organization like JSON, and unstructured data like text without a clear model. Metadata provides additional context about data to help with analysis. Big data is characterized by its large volume, velocity, and variety that makes it difficult to process with traditional tools.
Data pre-processing involves preparing raw data for machine learning models through several key steps:
1) Getting the raw dataset from various sources, 2) Importing necessary libraries, 3) Importing and storing large datasets in the cloud, 4) Cleaning data by handling missing values through techniques like deletion or approximation, 5) Encoding categorical variables as numbers, 6) Splitting the dataset into training and test sets, and 7) Feature scaling to normalize variable values for model training. These steps ensure the data is in a suitable format for building accurate machine learning models.
This document discusses data preparation, which is an important step in the knowledge discovery process. It covers topics such as outliers, missing data, data transformation, and data types. The goal of data preparation is to transform raw data into a format that will best expose useful information and relationships to data mining algorithms. It aims to reduce errors and produce better and faster models. Common tasks involve data cleaning, discretization, integration, reduction and normalization.
Data preprocessing is a technique used to prepare raw data for analysis by cleaning it, integrating data from multiple sources, reducing redundant features, and transforming the data. It involves techniques such as data cleaning to handle missing or inconsistent values, data integration to merge different data sources, data transformation such as normalization, and data reduction like aggregation or dimensionality reduction. Preprocessing resolves issues in raw data to prepare it for further analysis by techniques like data mining algorithms.
Presentation given to the BCS Data Management Specialist Group on 10th April 2018.
Data quality “tags” are a means of informing decision makers about the quality of the data they use within information systems. Unfortunately, these tags have not been successfully adopted because of the expense of maintaining them. This presentation will demonstrate an alternative approach that achieves improved decision making without the costly overheads.
Data preprocessing involves cleaning, transforming, and reducing raw data to prepare it for data mining and analysis. It addresses issues like missing values, inconsistent data, and reducing data size. The key goals of data preprocessing are to handle data problems, integrate multiple data sources, and reduce data size while maintaining the same analytical results. Major tasks involve data cleaning, integration, transformation, and reduction.
Similar to Data preprocessing using Machine Learning (20)
This unit explains cartesian coordinate system. This unit also explains different types of coordinate systems like one dimensional, two dimensional and three dimensional system
buy a fake University of London diploma supplementGlethDanold
Website: https://www.fakediplomamaker.shop/
Email: diplomaorder2003@gmail.com
Telegram: @fakeidiploma
skype: diplomaorder2003@gmail.com
wechat: jasonwilliam2003
buy bachelor degree from https://www.fakediplomamaker.shop/ to be competitive. Even if you are not already working and you havve just started to explore employment opportunities buy UK degree, buy masters degree from USA, buy bachelor degree from Australia, fake Canadian diploma where to buy diploma in Canada, It's still a great idea to purchase your degree and get a head start in your career. While many of the people your age will enlist in traditional programs and spend years learning you could accumulate valuable working experience. By the time they graduate you will have already solidified a respectable resume boasting both qualification and experience.
Structural Dynamics and Earthquake Engineeringtushardatta
Slides are prepared with a lot of text material to help young teachers to teach the course for the first time. This also includes solved problems. This can be used to teach a first course on structural dynamics and earthquake engineering. The lecture notes based on which slides are prepared are available in SCRIBD.
In the global energy equation, the IT industry is not yet a major contributor to global warming, but it is increasingly significant. From an engineering standpoint we can achieve huge energy saving by replacing electronic signal processing with optical techniques for routing and switching, whilst longer fibre spans in the local loop offer further reductions. The mobile industry on the other hand has engineered 5G systems demanding ~10kW/tower due to signal processing and beam steering technologies. This sees some countries (i.e. China) closing cell sites at night to save money. So, what of 6G? The assumption that all surfaces can be smart signal regenerators with beam steering looks be a step too far and it may be time for a rethink!
On the extreme end of the scale we have AWS planning to colocate their latest AI data centre (at 1GW power consumption) along side two nuclear reactors because it needs 40% of their joint output. Google and Microsoft are following the AWS approach and reportedly in negotiation with nuclear plant owners. Needless to say that AI train ing sessions and usage have risen to dominate the top of the IT demand curve. At this time, there appears to be no limits to the projected energy demands of AI, but there is a further contender in this technology race, and that is the IoT. In order to satisfy the ecological demands of Industry 4.0/Society 5.0 we need to instrument and tag ‘Things’ by the Trillion, and not ~100 Billion as previously thought!
Now let’s see, Trillions of devices connected to the internet with 5G, 4G, WiFi, BlueTooth, LoRaWan et al using >100mW demands more power plants…
Computer Vision and GenAI for Geoscientists.pptxYohanes Nuwara
Presentation in a webinar hosted by Petroleum Engineers Association (PEA) in 28 July 2023. The topic of the webinar is computer vision for petroleum geoscience.
Artificial Intelligence Imaging - medical imagingNeeluPari
10 stages of Artificial Intelligence,
Artificial intelligence (AI) has made significant advancements in the field of medical imaging, offering valuable tools and capabilities to improve diagnostics, treatment planning, and patient care. Here are several ways AI is used in medical imaging
BLW vocational training mechanical production workshop report.
Data preprocessing using Machine Learning
1. Dr. Gopal Sakarkar,
IEEE-CIS Member, Ph.D(CSE)
Department of AI and Machine Learning ,
G H RaisoniCollegeof Engineering , Nagpur
Data Pre-processing Services
using
Machine Learning Algorithms
7. What is Machine Learning?
• According to Arthur Samuel(1959), Machine Learning algorithms enable the
computers to learn from data, and even improve themselves, without being
explicitly programmed.
• Machine learning (ML) is a category of an algorithm that allows software
applications to become more accurate in predicting outcomes without being
explicitly programmed.
• The basic premise of machine learning is to build algorithms that can receive
input data and use statistical analysis to predict an output while updating
outputs as new data becomes available.
9. Types of Machine Learning
Supervised Learning Unsupervised Learning
MachineLearningAlgorithms
10. Where is Data Cleaning used?
Machine Learning Life Cycle
11. Data Pre-processing
• Data preprocessing is an important step in ML
• The phrase "garbage in, garbage out" is particularly applicable to data
mining and machine learning projects.
• It involves transforming raw data into an understandable format.
• Real-world data is often incomplete, inconsistent, and/or lacking in
certain behaviors or trends, and is likely to contain many errors.
• Data preprocessing is a proven method of resolving such issues
13. Why Data Pre-processing?
• A manager at All Electronics and have been charged with analyzing the company's data with
respect to the sales at a branch.
• He carefully inspect the company's database and data warehouse, identifying dimensions to be
included, such as item, price, units sold, and session .
• He notice that several of the attributes for various tuples have no recorded value. For analysis,
he would like to include information.
• In other words, the data he wish to analyze by machine learning techniques is incomplete,
noisy and inconsistent.
14. Why Data Pre-processing?
Item Price Unit Sold Session
TV 7200 44 All
Fan 480 27 Summer
Tube light 54 30 All
AC 27000 38
Fridge 40 Summer
Switches 58 35
2 mm Wire 520 All
Backup
Light 790 48 Winter
Fan
Regulator 83 50 All
Bulb 87 37 Rainy Session
15. What do you mean by data Pre-processing ?
• It is cleaning and explorating data for analysis
• Prepping data for modeling
• Modeling in Python requires numerical input
• Data preprocessing is a technique that involves transforming raw data into an understandable
format.
• Data preprocessing is a proven method of resolving such issues.
16. Data Understanding : Relevance of data
• What data is available for the task?
• Is this data relevant?
• Is additional relevant data available?
• How much historical data is available?
17. Data Understanding: Quantity of data
• Number of instances (records, objects)
• Rule of thumb: 5,000 or more desired
• if less, results are less reliable; use special methods (boosting, …)
• Number of attributes (fields)
• Rule of thumb: for each attribute, 10 or more instances
• If more fields, use feature reduction and selection
• if very unbalanced, use sampling
20. • Data Cleaning
Data cleaning is process of fill in missing values, smoothing the noisy data, identify or
remove outliers, and resolve inconsistencies.
• Data Integration
Integration of multiple databases, data cubes, or files.
• Data Transformation
Data transformation is the task of data normalization and aggregation.
Data Pre-processing Steps
21. • Data Reduction
Process of reduced representation in volume but produces the same or similar analytical
results.
• Data Discretization
Part of data reduction but with particular importance, especially for numerical data.
Data Pre-processing Steps
23. Data Cleaning
• Importance
Data cleaning is the number one problem during working with large data.
Data Cleaning Tasks
• Fill in missing values
• Identify outliers and smooth out noisy data
• Correct inconsistent data
• Resolve redundancy caused by data integration
24. Data Cleaning: Missing Data
• Data is not always available
E.g., while admission filling form by student at the time of admission,
he might be don’t known local guardian contact number.
• Missing data may be due to
equipment malfunction
inconsistent with other recorded data and thus deleted
data not entered due to misunderstanding
certain data may not be considered important at the time of entry
no register history or changes of the data
expansion of data schema
25. How to Handle Missing Data?
• Ignore the tuple (loss of information)
• Fill in missing values manually: tedious, infeasible?
• Fill in it automatically with
a global constant : e.g., unknown, a new class?!
Imputation: Use the attribute mean to fill in the missing value,
Use the most probable value to fill in the missing value.
26. Noisy Data
• Noise: random error or variance in a measured variable
• Incorrect attribute values may due to
faulty data collection instruments
data entry problems
data transmission problems
technology limitation
inconsistency in naming convention
• Other data problems which requires data cleaning
duplicate records
incomplete data
inconsistent data
27. How to handle noisy data?
• Binning method:
first sort data and partition into (equi-depth) bins
then one can smooth by bin means, smooth by bin median, smooth
by bin boundaries, etc.
• Combined computer and human inspection
detect suspicious values and check by human
28. Binning Methods for Data Smoothing
• Sorted data for price (in dollars): 4, 8, 9, 15, 21, 21, 24, 25, 26, 28, 29, 34
* Partition into (equi-depth) bins:
-Bin 1: 4, 8, 9, 15
-Bin 2: 21, 21, 24, 25
-Bin 3: 26, 28, 29, 34
* Smoothing by bin means:
-Bin 1: 9, 9, 9, 9 (4+8+9+15/4) =9
-Bin 2: 23, 23, 23, 23 (21+21+24+25/4)=23
-Bin 3: 29, 29, 29, 29 (26+28+29+34/4)=29
* Smoothing by bin boundaries:
-Bin 1: 4, 4, 4, 15
-Bin 2: 21, 21, 25, 25
-Bin 3: 26, 26, 26, 34
29. Data Integration
Data integration:
Its combines data from multiple sources
• Schema integration
Integrate metadata from different sources
Entity identification problem: identify real world entities from
multiple data sources, e.g., A.cust-id ≡ B.cust-#
• Detecting and resolving data value conflicts
• for the same real world entity, attribute values from different
sources are different, e.g., different scales, metric vs. British units
• Removing duplicates and redundant data
30. Data Transformation
Data Transformation
• Smoothing: remove noise from data
• Normalization: scaled to fall within a small, specified range
• Attribute/feature construction
New attributes constructed from the given ones
• Aggregation: summarization
Integrate data from different sources (tables)
31. Data Reduction
• Data is too big to work with
Too many instances
too many features (attributes)
Data Reduction
Obtain a reduced representation of the data set that is much smaller
in volume but yet produce the same (or almost the same) analytical
results (easily said but difficult to do)
• Data reduction strategies
Dimensionality reduction — remove unimportant attributes
Aggregation and clustering –
Remove redundant or close associated ones
Sampling
32. Data Reduction
Clustering
• Partition data set into clusters, and one can store cluster
representation only.
• Can be very effective if data is clustered but not if data is dirty.
• There are many choices of clustering and clustering algorithms.
33. Data Reduction
Sampling
• Choose a representative subset of the data
Simply selecting random sampling may have improve
performance in the presence of scenario .
• Develop adaptive sampling methods
Stratified sampling:
Approximate the percentage of each class (or subpopulation of
interest) in the overall database
35. Data Discretization
• Discretization is a process that transforms quantitative data into
qualitative data.
• It significantly improve the quality of discovering knowledge.
• It reduces the running time of various machine learning tasks such as
association rule discovery, classification, clustering and prediction.
• It reduce the number of values for a given continuous attribute by
dividing the range of the attribute into intervals.
• Interval labels can then be used to replace actual data values