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Performing EDA on a dataset with missing features

There are a lot of techniques through which you can fill the missing values. Some of them are: 1.) Replacing with mean, median or mode as you correctly pointed out. 2.) Replacing with a constant value ...
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Data preprocessing methods

Here are some. Dealing with the variable Types. Dealing with Missing data Encoding categorical variables Categorical variable — cardinality Categorical variable — rare labels Dealing with Outliers ...
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Should I Impute target values?

Data Imputation of the target variable makes the model BIAS. A small correction is not to use label encoder for predictors. Label Encoder to be used for only target variables if they are categorical. ...
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how to deal with columns that has different value in only 1 or 2 rows?

In other words, you have sparse binary features. A vast majority of the data is zeros. The remaining data are ones. One option is to transform the features to be denser. This can be done with ...
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How can we predict a value after several rows of data?

Try to make all the rows of the week one row. Considering the max number of rows/weeks: Week 1; $x_1, x_2, x_3...x_{90}, v_1, v_2, v_3...v_{90}...z_1, z_2, z_3...z_{90}; y_1$ Week 2; $x_1, x_2, x_3......
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