I am working on logistic regression project where I have 850 observations and 8 variables and in this, I found 150 missing values and I have decided to use these values as test data. How can I take only missing values as test data in python?
I am still learning data science if there's a mistake in this approach please let me know.
Thank you :)
You could use the pd.isna() from pandas library.
It will return a boolean array that you can use for filtering your data.
You can select all rows, having any missing value in that, using following code
df[df.isnull().values.any(axis=1)]
I do not recommend you to use all data with missing values for testing. You should either fill the missing values completely or at least partial values should be filled in the test dataset.
Let's see what other Machine Learning experts advise you.
Related
I am a beginner in Machine Learning, my point is..how should i encode the column "OECDSTInterbkRate"? I don't know how to replace the missing values and especially with what. Should I just delete them? Or replace them with the mean / median of the values?
There are many approaches to this issue.
The simplest: if you have huge amount of data - drop NaNs.
Replace the NaNs with mean/median/etc of the whole non-NaN dataset or the dataset grouped by one or several columns. E.g. for you dataset you can fill the Australia NaNs with a mean for Australian non-NaNs. And the same for other countries.
A common approach is to create another indicator column after the imputation of NaNs which keeps the indices where the missing data was replaced with a value. This column then is taken as yet another input to your ML algorithm.
Take a look at the docs (assuming you work with Pandas) - the developers of the library have already created some tools for the missing data: https://pandas.pydata.org/pandas-docs/stable/user_guide/missing_data.html
There's no specific answer to your question, it's a general problem in statistics which is called "imputation". Depending on the application the answer could be many things.
There are few alternatives that comes to mind first to solve your problem, but don't forget that "no data" is almost always better than "bad/wrong data". If you have more than enough rows without the rows with NaNs, you may simply drop them. Otherwise you can consider the following:
Can you mathematically calculate the column that you need by the other columns that you already have in your dataset? If so, you have your answer.
Check the correlation of the particular column by using it's non-missing valued rows with the other columns and see if they are highly correlated. If so, you might just as well try dropping the whole column(might not be always a good idea but it's generally a good idea).
Can you create an estimator(such as a regression model) to predict the missing values by learning the pattern using the values that you already have and by using the other columns with a really good accuracy? Well you might have an answer (need benchmarking with the following). Please keep in mind that this is a very risky operation that could give bad estimations and decrease the performance of your overall model. Try this only if your estimations are really good!
Is it a regression problem? Using the statistical mean could be a good idea.
Is it a classification problem? Using median could be a good idea.
In some cases using mode might also be a good idea depending on the distribution.
I suggest that you try all the things out and see which one works better because there's really not a concrete answer to your problem. You can create a machine learning model without using the column and use it's performance as a baseline, and carry out a performance(accuracy) benchmarking for all the steps compared to the baseline.
Note: I am just a graduate student with some insights, please comment out if anything I said is not correct!
I am facing a dilemma with a project of mine. Few of the variables don't have enough data that means almost 99% data observations are missing.
I am thinking of couple of options -
Impute missing value with mean/knn imputation
Impute missing value with 0.
I couldn't think of anything in this direction. If someone can help that would be great.
P.S. I am not feeling comfortable using mean imputation when 99% of the data is missing. Does someone have a reasoning for that? kindly let me know.
Data has 397576 Observations out of which below are the missing values
enter image description here
99% of the data is missing!!!???
Well, if your dataset has less than 100,000 examples, then you may want to remove those columns instead of imputing through any methods.
If you have a larger dataset then using mean imputing or knn imputing would be ...OK. These methods don't catch the statistics of your data and can eat up memory. Instead use Bayesian methods of Machine Learning like fitting a Gaussian Process through your data or a Variational Auto-Encoder to those sparse columns.
1.) Here are a few links to learn and use gaussian processes to samples missing values from the dataset:
What is a Random Process?
How to handle missing values with GP?
2.) You can also use a VAE to impute the missing values!!!
Try reading this paper
I hope this helps!
My first question to give a good answer would be:
What you are actually trying to archive with the completed data?
.
People impute data for different reasons and the use case makes a big difference for example you could use imputation as:
Preprocessing step for training a machine learning model
Solution to have a nice Graphic/Plot that does not have gaps
Statistical inference tool to evaluate scientific or medical studies
99% of missing data is a lot - in most cases you can expect, that nothing meaningful will come out of this.
For some variables it still might make sense and produce at least something meaningful - but you have to handle this with care and think a lot about your solution.
In general you can say, imputation does not create entries out of thin air. A pattern must be present in the existing data - which then is applied to the missing data.
You probably will have to decide on a variable basis what makes sense.
Take your variable email as an example:
Depending how your data - it might be that each row represents a different customer that has a specific email address. So that every row is supposed to be a unique mail address. In this case imputation won't have any benefits - how should the algorithm guess the email. But if the data is structured differently and customers appear in multiple rows - then an algorithm can still fill in some meaningful data. Seeing that Customer number 4 always has the same mail address and filling it for rows where only customer number 4 is given and the mail is missing.
I'm currenlty working on a project and I have to fill some missing values.
I use Python, and I saw that there is an algorithm which can do missing data imputation.
This algorithm is called Nipals. So, I decided to search a way to use it and I saw that statsmodels.multivariate.pca.PCA could help me.
I have a numpy array named A with n rows and p columns. A has some missing values which are NaN values. I would like to use PCA to fill A. But, there are no examples which can help me to do it.
Can someone help me to fill A using Nipals algorithm ?
Thank you.
N.B. Sorry, I'm a French beginner, it's not easy for me to use english documentations..
I find a way to fill missing values.
Let's assume you have a numpy array named A
from statsmodels.multivariate.pca import PCA
pc = PCA(data=A,ncomp=1, missing='fill-em')
A=pc._adjusted_data
Enjoy !
You can also use another way to fill missing values : mean, median, k-neighbors, mcmc (Monte Carlo Markov Chain), most frequent value...
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Hi ,
Attached is the data, can you please help me to handle the missing data in the "Outlet_Size" column.
So that i can use this complete data for preparing the datascience models.
Thanks,
These are one of the major challenges of Data Mining problems (or Machine Learning). YOU decide what to do with the missing data based on PURE EXPERIENCE. You mustn't look at Data Science as a blackbox that follows a series of steps to be successful at it!
Some guidelines about missing data.
A. If more than 40% of the data is missing from a column, drop it! (Again, the 40% depends on what type of problem you're working with! If the data is super crucial or its very trivial that you can ignore it).
B. Check if there is someway you can impute the missing data from the internet. You're looking at item weight! If there is anyway you could know which product you're dealing with instead of hashed coded Item_Identifier, then you can always literally Google it and figure it out.
C. Missing data can be classified into two types:
MCAR: missing completely at random. This is the desirable scenario in case of missing data.
MNAR: missing not at random. Missing not at random data is a more serious issue and in this case it might be wise to check the data gathering process further and try to understand why the information is missing. For instance, if most of the people in a survey did not answer a certain question, why did they do that? Was the question unclear?
Assuming data is MCAR, too much missing data can be a problem too. Usually a safe maximum threshold is 5% of the total for large datasets. If missing data for a certain feature or sample is more than 5% then you probably should leave that feature or sample out. We therefore check for features (columns) and samples (rows) where more than 5% of the data is missing using a simple function
D. As posted in the comments, you can simply drop the rows using df.dropna() or fill them with infinity, or fill them with mean using df["value"] = df.groupby("name").transform(lambda x: x.fillna(x.mean()))
This groups the column value from dataframe df by category name, finds the mean in each category and fills the missing value in value with the corresponding mean of that category!
E. Apart from just either dropping missing values, replacing with mean or median, there are other advanced regression techniques you can use that has a way to predict missing values and fill it, E.G (mice: Multivariate Imputation by Chained Equations), you should browse and read more about where advanced imputation technique will be helpful.
The accepted answer is really nice.
In your specific case I'd say either drop the column or assign a new value called Missing. Since that's a Categorical variable, there's a good chance it ends up going into a OneHot or Target Encoder (or being understandable by the model as a category directly). Also, the fact the value is NaN is an info itself, it can come from multiple factors (from bad data to technical difficulties getting an answer, etc). Be careful and watch this doesn't brings bias or some information you shouldn't know (example : the products have NaN due to not being into a certain base, thing that will never happen in a real situation, which will make your result non-representative of a true situation)
The column "Outlet_Size" contains the categorical data, so instead of dropping the data use measures to fill data.
Since it is categorical data use Measures of Central Tendency, Mode.
Use mode to find which category occurs more or frequently and fill the column with the corresponding value.
Code:
Dataframe['Outlet_Size'].mode()
Datarame['Outlet_Size'].fillna(Dataframe['Outlet_Size'].mode(), inplace=True)
First thanks for reading me and thanks a lot if you can give any clue to help me solving this.
As I'm new to Scikit-learn, don't hesitate to provide any advice that can help me to improve the process and make it more professional.
My goal is to classify data between two categories. I would like to find a solution that would give me the most precise result. At the moment, I'm still looking for the most suitable algorithm and data preprocessing.
In my data I have 24 values : 13 are nominal, 6 are binarized and the others are continuous. Here is an example of a line
"RENAULT";"CLIO III";"CLIO III (2005-2010)";"Diesel";2010;"HOM";"_AAA";"_BBB";"_CC";0;668.77;3;"Fevrier";"_DDD";0;0;0;1;0;0;0;0;0;0;247.97
I have around 900K lines for learning and I do my test over 100K lines
As I want to compare several algorithm implementations, I wanted to encode all the nominal values so it can be used in several Classifier.
I tried several things:
LabelEncoder : this was quite good but it gives me ordered values that would be miss-interpreted by the classifier.
OneHotEncoder : if I understand well, it is quite perfect for my needs because I could select the column to binarize. But as I have a lot of nominal values, it always goes in MemoryError. Moreover, its input must be numerical so it is compulsory to LabelEncode everything before.
StandardScaler : this is quite useful but not for what I need. I decided to integrate it to scale my continuous values.
FeatureHasher : first I didn't understand what it does. Then, I saw that it was mainly used for Text analysis. I tried to use it for my problem. I cheated by creating a new array containing the result of the transformation. I think it was not built to work that way and it was not even logical.
DictVectorizer : could be useful but looks like OneHotEncoder and put even more data in memory.
partial_fit : this method is given by only 5 classifiers. I would like to be able to do it with Perceptron, KNearest and RandomForest at least so it doesn't match my needs
I looked on the documentation and found these information on the page Preprocessing and Feature Extraction.
I would like to have a way to encode all the nominal values so that they will not be considered as ordered. This solution can be applied on large datasets with a lot of categories and weak resources.
Is there any way I didn't explore that can fit my needs?
Thanks for any clue and piece of advice.
To convert unordered categorical features you can try get_dummies in pandas, more details can refer to its documentation. Another way is to use catboost, which can directly handle categorical features without transforming them into numerical type.