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Tired of working with messy data? Did you know that most of a data scientist's time is spent in finding, cleaning and reorganizing data?! Well turns out you can clean your data in a smart way! In this course Dealing with Missing Data in Python, you'll do just that! You'll learn to address missing values for numerical, and categorical data as well as time-series data. You'll learn to see the patterns the missing data exhibits! While working with air quality and diabetes data, you'll also learn to analyze, impute and evaluate the effects of imputing the data.
The Problem With Missing DataFree
Get familiar with missing data and how it impacts your analysis! Learn about different null value operations in your dataset, how to find missing data and summarizing missingness in your data.Why deal with missing data?50 xpSteps for treating missing values50 xpNull value operations100 xpFinding Null values100 xpHandling missing values50 xpDetecting missing values100 xpReplacing missing values100 xpReplacing hidden missing values100 xpAnalyze the amount of missingness50 xpAnalyzing missingness percentage100 xpVisualize missingness100 xp
Does Missingness Have A Pattern?
Analyzing the type of missingness in your dataset is a very important step towards treating missing values. In this chapter, you'll learn in detail how to establish patterns in your missing and non-missing data, and how to appropriately treat the missingness using simple techniques such as listwise deletion.Is the data missing at random?50 xpGuess the missingness type100 xpDeduce MNAR100 xpFinding patterns in missing data50 xpFinding correlations in your data100 xpIdentify the missingness type50 xpVisualizing missingness across a variable50 xpFill dummy values100 xpGenerate scatter plot with missingness100 xpWhen and how to delete missing data50 xpDelete MCAR100 xpWill you delete?100 xp
Embark on the world of data imputation! In this chapter, you will apply basic imputation techniques to fill in missing data and visualize your imputations to be able to evaluate your imputations' performance.Mean, median & mode imputations50 xpMean & median imputation100 xpMode and constant imputation100 xpVisualize imputations100 xpImputing time-series data50 xpFilling missing time-series data100 xpImpute with interpolate method100 xpVisualizing time-series imputations50 xpVisualize forward fill imputation100 xpVisualize backward fill imputation100 xpPlot interpolations100 xp
Advanced Imputation Techniques
Finally, go beyond simple imputation techniques and make the most of your dataset by using advanced imputation techniques that rely on machine learning models, to be able to accurately impute and evaluate your missing data. You will be using methods such as KNN and MICE in order to get the most out of your missing data!Imputing using fancyimpute50 xpKNN imputation100 xpMICE imputation100 xpImputing categorical values50 xpOrdinal encoding of a categorical column100 xpOrdinal encoding of a DataFrame100 xpKNN imputation of categorical values100 xpEvaluation of different imputation techniques50 xpAnalyze the summary of linear model100 xpComparing and choosing the best adjusted R-squared100 xpComparing density plots100 xpConclusion50 xp
In the following tracksPython Toolbox
Suraj DonthiSee More
Deep Learning & Computer Vision Consultant
Suraj is a Deep Learning practitioner with experience in applying deep learning and machine algorithms to solve complex problems in the domains of automotive, retail, surveillance, biomedical image processing, trading as well as analytics. He has worked with clients across the globe to provide reliable machine learning solutions.