Cleaning Data in CSV file for ML Model - machine-learning

Question

I'm trying to clean my data in jupyterlab by watching several tutorials, but I keep getting one or the other error every time. So I thought I'd come on stack overflow and ask if someone can help me.
This is the csv file I want to clean: https://1drv.ms/u/s!AvOXB8kb-IHBgjaveis044GVoPpk
I'm building a machine learning model so I want to convert all the object values, but I don't know how to.
EDIT: I tried cleaning the data from scratch.
My code input:
import pandas as pd
from sklearn.tree import DecisionTreeClassifier
criminal_data = pd.read_csv('database2.csv')
X = criminal_data.drop(columns=['Agency Type', 'City', 'State',
'Crime Solved'])
y = criminal_data['City']
model = DecisionTreeClassifier()
model.fit(X, y)
criminal_data
The error message:
ValueError Traceback (most recent call
last)
<ipython-input-117-4b6968f9994f> in <module>
6 y = criminal_data['City']
7 model = DecisionTreeClassifier()
----> 8 model.fit(X, y)
9 criminal_data
~\anaconda3\lib\site-packages\sklearn\tree\_classes.py in fit(self, X, y, sample_weight, check_input, X_idx_sorted)
896 """
897
--> 898 super().fit(
899 X, y,
900 sample_weight=sample_weight,
~\anaconda3\lib\site-packages\sklearn\tree\_classes.py in fit(self, X, y, sample_weight, check_input, X_idx_sorted)
154 check_X_params = dict(dtype=DTYPE, accept_sparse="csc")
155 check_y_params = dict(ensure_2d=False, dtype=None)
--> 156 X, y = self._validate_data(X, y,
157 validate_separately=(check_X_params,
158 check_y_params))
~\anaconda3\lib\site-packages\sklearn\base.py in _validate_data(self, X, y, reset, validate_separately, **check_params)
428 # :(
429 check_X_params, check_y_params =
validate_separately
--> 430 X = check_array(X, **check_X_params)
431 y = check_array(y, **check_y_params)
432 else:
~\anaconda3\lib\site-packages\sklearn\utils\validation.py in inner_f(*args, **kwargs)
61 extra_args = len(args) - len(all_args)
62 if extra_args <= 0:
---> 63 return f(*args, **kwargs)
64
65 # extra_args > 0
~\anaconda3\lib\site-packages\sklearn\utils\validation.py in
check_array(array, accept_sparse, accept_large_sparse, dtype, order,
copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator)
614 array = array.astype(dtype, casting="unsafe",
copy=False)
615 else:
--> 616 array = np.asarray(array, order=order, dtype=dtype)
617 except ComplexWarning as complex_warning:
618 raise ValueError("Complex data not supported\n"
~\anaconda3\lib\site-packages\numpy\core\_asarray.py in asarray(a, dtype, order, like)
100 return _asarray_with_like(a, dtype=dtype, order=order,
like=like)
101
--> 102 return array(a, dtype, copy=False, order=order)
103
104
~\anaconda3\lib\site-packages\pandas\core\generic.py in __array__(self, dtype)
1897
1898 def __array__(self, dtype=None) -> np.ndarray:
-> 1899 return np.asarray(self._values, dtype=dtype)
1900
1901 def __array_wrap__(
~\anaconda3\lib\site-packages\numpy\core\_asarray.py in asarray(a, dtype,
order, like)
100 return _asarray_with_like(a, dtype=dtype, order=order,
like=like)
101
--> 102 return array(a, dtype, copy=False, order=order)
103
104
ValueError: could not convert string to float: 'Anchorage'
​

Answer 1

You are trying to train your model with some data that is not numerical. Before using the model, you need to do encoding. You can try LabelEncoder for that.
from sklearn import preprocessing
le = preprocessing.LabelEncoder()
for column_name in X.columns:
if X[column_name].dtype == object:
X[column_name] = le.fit_transform(X[column_name])
else:
pass
If you have a combination of different data types in a row. Try below:
from sklearn import preprocessing
le = preprocessing.LabelEncoder()
for column_name in X.columns:
X[column_name] = X[column_name].replace(np.nan, 'none', regex=True)
X[column_name] = le.fit_transform(X[column_name].astype(str))