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How to deal with missing data in a SVM model from sklearn?
I am using SVM classifier from sklearn:
from sklearn.feature_extraction import DictVectorizer
vec_features = DictVectorizer()
df = pd.read_csv(file)
vectorized_features = vec_features.fit_transform(df)
This is the code so far.
I am now working on cleaning up my dataset.
In the dataframe df, some of the columns may have missing values. I want to keep the dataframe where all of the values are there, regardless if they have missing values or not.
How can I do that?
Thank you!
A:
sklearn’s vectorizer doesn’t handle missing data.
You need to do it manually, for example:
Use a Pandas DataFrame with 0-s where the column value is missing and NaN otherwise
Fill the NaN values to the mean value of that column (or, in your case, the train set average value)
And this is how you do it:
df_clean = df.dropna(subset=[‚column_name‘], how=’any‘, inplace=True)
df_clean[‚column_name‘] = df_clean[‚column_name‘].fillna(df_clean[‚column_name‘].mean())
Note that the number of non-missing values must be the same as the number of columns you have.
To convert the DataFrame back to a CSV use:
df_clean.to_csv(‚your_csv.csv‘, sep=“\t“, encoding=’utf-8′, index=False)
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