A LITTLE GUIDE TO HANDLING MISSING DATA
Having any Feature missing more than 5-10% of its values? you should consider it to be missing data or feature with high absence rateπ
How can you handle these missing values, ensuring you dont loose important part of your dataπ€·ββοΈ
Not a problemπ. Here are important facts you must knowπ
βοΈInstances with missing values for all features should be eliminated
βοΈFeatures with high absence rate should either be eliminated or filled with values
βοΈMissing values can be replaced using Mean Imputation or Regression Imputation
βοΈ Be careful with mean imputation for it may introduce bias as it evens out all instances
βοΈRegression Imputation might overfit your model
βοΈMean and Regression Imputation can't be applied to Text features with missing values
βοΈText Features with missing values can be eliminated if not needed in data
βοΈImportant Text Features with Missing values can be replaced with a new class or category labelled as uncategorized
Having any Feature missing more than 5-10% of its values? you should consider it to be missing data or feature with high absence rateπ
How can you handle these missing values, ensuring you dont loose important part of your dataπ€·ββοΈ
Not a problemπ. Here are important facts you must knowπ
βοΈInstances with missing values for all features should be eliminated
βοΈFeatures with high absence rate should either be eliminated or filled with values
βοΈMissing values can be replaced using Mean Imputation or Regression Imputation
βοΈ Be careful with mean imputation for it may introduce bias as it evens out all instances
βοΈRegression Imputation might overfit your model
βοΈMean and Regression Imputation can't be applied to Text features with missing values
βοΈText Features with missing values can be eliminated if not needed in data
βοΈImportant Text Features with Missing values can be replaced with a new class or category labelled as uncategorized
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microsoft/Data-Science-For-Beginners
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Azure Cloud Advocates at Microsoft are pleased to offer a 10-week, 20-lesson curriculum all about Data Science. Each lesson includes pre-lesson and post-lesson quizzes, written instructions to complete the lesson, a solution, and an assignment. Our project-based pedagogy allows you to learn while building, a proven way for new skills to 'stick'.
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6.3 MB
The Machine Learning Workshop
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Author: Hyatt Saleh
Pages: 285
Pandas_Cheat_Sheet.pdf
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THE PANDAS CHEAT SHEET
A well detailed guide to data wrangling using pandas
A well detailed guide to data wrangling using pandas
Reasons Why Data Goes Missing
Understanding the reason for the missing data in your dataset is important because it helps you determine the type of missing data and what you need to do about it. Lets get our brain to grasp this concept shall we?ππ
Missing Completely at Random(MCAR): This is a fact that a certain missing value has nothing to do with its hypothetical value and values of other variables. eg:
You collect data on end-of-year holiday spending patterns. You survey adults on how much they spend annually on gifts for family and friends in dollar amounts.
You note that there are a few missing values in your holiday spending dataset. Some people started answering your survey but dropped out or skipped a question.
However, you note that you have data points from a wide distribution, ranging from low to high values.
Therefore, you conclude that the missing values arenβt related to any specific holiday spending amount range.
Missing at Random(MAR):This means that the propensity for a data point to be missing is unrelated to the missing data but related to some observed data. eg:
You repeat your data collection with a new group. You notice that there are more missing values for adults aged 18β25 than for other age groups.
But looking at the observed data for adults aged 18β25, you notice that the values are widely spread. Itβs unlikely that the missing data are missing because of the specific values themselves.
Instead, some younger adults may be less inclined to reveal their holiday spending amounts for unrelated reasons (e.g., more protective of their privacy).
Missing Not at Random(MNAR): This is data that is neither MAR nor MCAR (i.e. the value of the variable that's missing is related to the reason it's missing). eg:
If some participants with low incomes avoid reporting their holiday spending amounts because they are low in your datast, then this is a MNAR problem
Understanding the reason for the missing data in your dataset is important because it helps you determine the type of missing data and what you need to do about it. Lets get our brain to grasp this concept shall we?ππ
Missing Completely at Random(MCAR): This is a fact that a certain missing value has nothing to do with its hypothetical value and values of other variables. eg:
You collect data on end-of-year holiday spending patterns. You survey adults on how much they spend annually on gifts for family and friends in dollar amounts.
You note that there are a few missing values in your holiday spending dataset. Some people started answering your survey but dropped out or skipped a question.
However, you note that you have data points from a wide distribution, ranging from low to high values.
Therefore, you conclude that the missing values arenβt related to any specific holiday spending amount range.
Missing at Random(MAR):This means that the propensity for a data point to be missing is unrelated to the missing data but related to some observed data. eg:
You repeat your data collection with a new group. You notice that there are more missing values for adults aged 18β25 than for other age groups.
But looking at the observed data for adults aged 18β25, you notice that the values are widely spread. Itβs unlikely that the missing data are missing because of the specific values themselves.
Instead, some younger adults may be less inclined to reveal their holiday spending amounts for unrelated reasons (e.g., more protective of their privacy).
Missing Not at Random(MNAR): This is data that is neither MAR nor MCAR (i.e. the value of the variable that's missing is related to the reason it's missing). eg:
If some participants with low incomes avoid reporting their holiday spending amounts because they are low in your datast, then this is a MNAR problem
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Deep Learning Prerequisites: The Numpy Stack in Python V2
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Duration β°: 1hr 59min
Created by Lazy Programmer Team, Lazy Programmer Inc.
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presentation given by π¨βπ«: MITβs Brandon Leshchinskiy
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Duration β°: 31 hours worth of material
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Resource: Class Central
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by Kaggle
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By: Azure Cloud Advocates at Microsoft
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Repo: https://microsoft.github.io/Data-Science-For-Beginners/#/?id=lessons
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Here's a brain friendly guide to learning SQL for beginners
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Statistics Guide for Data Science
Learning Statistics for Data Science can be quite overwhelming for beginners without a Statistics background. One can get confused on which topics to learn or books to read up to equip their knowledge
You don't have to learn it all. Here are essential topics you can learn
1) Know what a p value is and its limitations
2) Linear Regression and its Assumptions
3) Different Statistical Distributions and when to use them
4) Mean, Variance for Normal, Poisson, and Uniform Distribution
5) Sampling Techniques and Common Designs(eg: A/B)
6) Bayes Theorems and it's application
7) Measurements and Interpretation of Confidence Intervals
8) Logistics Regressions and ROC curves
9) Resampling(Cross Validation and Bootstrapping)
10) Tree Based Models
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Learning Statistics for Data Science can be quite overwhelming for beginners without a Statistics background. One can get confused on which topics to learn or books to read up to equip their knowledge
You don't have to learn it all. Here are essential topics you can learn
1) Know what a p value is and its limitations
2) Linear Regression and its Assumptions
3) Different Statistical Distributions and when to use them
4) Mean, Variance for Normal, Poisson, and Uniform Distribution
5) Sampling Techniques and Common Designs(eg: A/B)
6) Bayes Theorems and it's application
7) Measurements and Interpretation of Confidence Intervals
8) Logistics Regressions and ROC curves
9) Resampling(Cross Validation and Bootstrapping)
10) Tree Based Models
ββββββββββββββββββββ
Join @datascience_bds for more cool data science materials.
*This channel belongs to @bigdataspecialist group
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High quality data is key for building useful machine learning models. Models learn their behaviour from data. So, finding the right data is a big part of the work to build machine learning into your products.
This article gives a concise explanation on finding the right data for your models.
https://towardsdatascience.com/where-to-find-data-for-machine-learning-e375e2a515c8
High quality data is key for building useful machine learning models. Models learn their behaviour from data. So, finding the right data is a big part of the work to build machine learning into your products.
This article gives a concise explanation on finding the right data for your models.
https://towardsdatascience.com/where-to-find-data-for-machine-learning-e375e2a515c8
Medium
Where to find Data for Machine Learning
High quality data is key for building useful machine learning models