1 / 2

Data Wrangling and Cleaning Techniques (Handling Missing Data, Outliers) – Key Points

ExcelR's Data Science Course offers a comprehensive learning experience tailored to meet the demands of the industry.<br><br>Business name: ExcelR- Data Science, Data Analytics, Business Analytics Course Training Mumbai<br>Address: 304, 3rd Floor, Pratibha Building. Three Petrol pump, Lal Bahadur Shastri Rd, opposite Manas Tower, Pakhdi, Thane West, Thane, Maharashtra 400602<br>Phone: 09108238354 <br>Email: enquiry@excelr.com<br>

Saketh4
Download Presentation

Data Wrangling and Cleaning Techniques (Handling Missing Data, Outliers) – Key Points

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Data Wrangling and Cleaning Techniques (Handling Missing Data, Outliers)– KeyPoints • HandlingMissingData: • Missing data can occur due to various reasons, such as incomplete data collection or system errors. Data Science Course. Techniques to handle missing data include removing rows or columnswithmissingvalues(iftheimpactisminimal),imputingmissingdatawithmean, median,ormodevalues,orusingadvancedtechniqueslikeK-nearestneighbors(KNN) imputationor regressionimputation to predictthe missingvalues based onother available data. • DealingwithOutliers: • Outliersareextremevaluesthatdeviatesignificantlyfromtherestofthedataandcanskew analysisresults. Common techniques forhandling outliers include: • Removingoutliersiftheyaredeemedtobeerrors. • Winsorizing(limitingextremevalues). • Transforming data usinglog transformationsto reducethe impactof outliers. • - StatisticalmethodssuchasusingZ-scoresortheIQR(Interquartile Range) method to identifyand handle outliers. • DataStandardizationandNormalization: • Standardization (z-score normalization) and normalization (scaling data to a [0, 1] range) are techniques used to adjust the scale of data, especially when features have different units or magnitudes. This is important for algorithms that are sensitive to the scale of input data, such as machinelearning models.These methods helpensure fair comparisonsacross features. • RemovingDuplicates: • Duplicate data can occur due to errors in data collection or merging datasets from different sources.Identifyingandremovingduplicatesisanessentialdatacleaning step to prevent redundant information from affecting analysis outcomes. This can be achieved using tools like Python’s Pandas or SQL queries to check for identical rows and remove them. • DealingwithInconsistentData:

  2. Inconsistent data, such as variations in naming conventions, date formats, or categories, can leadtoinaccurateanalysis.DataScienceCourseinMumbai.Techniquesincludestring matching or regular expressions to correct naming inconsistencies, standardizing formats (e.g., date conversions), and consolidating similar categories into a single, unified format. Automated dataprofiling tools can help identify and rectifythese inconsistencies efficiently. Businessname:ExcelR-DataScience,DataAnalytics,BusinessAnalyticsCourseTraining Mumbai Address:304,3rdFloor,PratibhaBuilding.ThreePetrolpump,LalBahadurShastriRd, oppositeManas Tower, Pakhdi, Thane West, Thane, Maharashtra 400602 Phone:09108238354 Email:enquiry@excelr.com

More Related