Hi all,

I am currently practicing the practice problem in the Hackathon, Experiments with data. I am little confused in the last part.

```
le = LabelEncoder()
for var in categorical_variables:
train[var] = le.fit_transform(train[var])
for var in categorical_variables[:len(categorical_variables)-1]:
test[var] = le.fit_transform(test[var])
```

here we have converted the categorical variables to numeric codes. this is fine.

Now in the Test data Frame i am creating a new column “Income.Group” and assigning the predicted values to that column.

```
model = DecisionTreeClassifier(max_depth = 10,min_samples_leaf = 100, max_features = 'sqrt')
model.fit(train[independent_variable],train[dependent_variable])
predictions_train = model.predict(train[independent_variable])
predictions_test = model.predict(test[independent_variable])
test['Income.Group'] = predictions_test
test.to_csv('D:/AnalyticsVidya/Workshop/ttt.csv')
```

Now when i open the output csv file, it is showing the values in the numeric format (which is obvious as we converted the dataframe with LabelEncoder).

But, i i want to reconvert the categorical variables from Numeric back into the categories, how to do that. Basically, how to reverse the process done by the function LabelEncoder() ?