I have a dataframe that contains
user_id date browser conversion test sex age country
1 2015-12-03 IE 1 0 M 32.0 US
This is my entire code thus far!
data["country"].fillna("missing")
data["age"].fillna(-10000, inplace=True)
data["ads_channel"].fillna("missing")
data["sex"].fillna("missing")
data['date'] = pd.to_datetime(data.date)
columns = data.columns.tolist()
columns = [c for c in columns if c not in ["test"]]
from sklearn import tree
clf = tree.DecisionTreeClassifier(max_depth=2, min_samples_leaf = (len(data)/100) )
clf = clf.fit(data[columns],data["test"])
I am getting this error:
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-560-95a8a54aa939> in <module>()
4 from sklearn import tree
5 clf = tree.DecisionTreeClassifier(max_depth=2, min_samples_leaf = (len(data)/100) )
----> 6 clf = clf.fit(data[columns],data["test"])
C:UsersSnehaPriyaAnaconda2libsite-packagessklearnreeree.pyc in fit(self, X, y, sample_weight, check_input, X_idx_sorted)
152 random_state = check_random_state(self.random_state)
153 if check_input:
--> 154 X = check_array(X, dtype=DTYPE, accept_sparse="csc")
155 if issparse(X):
156 X.sort_indices()
C:UsersSnehaPriyaAnaconda2libsite-packagessklearnutilsvalidation.pyc in check_array(array, accept_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)
371 force_all_finite)
372 else:
--> 373 array = np.array(array, dtype=dtype, order=order, copy=copy)
374
375 if ensure_2d:
TypeError: float() argument must be a string or a number
I am still learning to code and I would like to know how to overcome this error.
Any help will be much appreciated!
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