I'm working on a project using Python(3.6) and Sklearn.I have done classifications but when I try to apply it for reshaping in order to use it with fit method of sklearn it returns an error.

Here's what I have tried:

# Get all the columns from dataframe
columns = data.columns.tolist()# Filter the columns to remove data we don't want
columns = [c for c in columns if c not in ["Class"] ]# store the variables we want to predicting on
target = "Class"
X = data.drop(target, 1)
Y = data[target]# Print the shapes of X & Y
print(X.shape)
print(Y.shape)# define a random state
state = 1# define the outlier detection method
classifiers = {"Isolation Forest": IsolationForest(max_samples=len(X),contamination=outlier_fraction,random_state=state),"Local Outlier Factor": LocalOutlierFactor(n_neighbors = 20,contamination = outlier_fraction)
}# fit the model
n_outliers = len(Fraud)for i, (clf_name, clf) in enumerate(classifiers.items()):# fit te data and tag outliersif clf_name == "Local Outlier Factor":y_pred = clf.fit_predict(X)scores_pred = clf.negative_outlier_factor_else:clf.fit(X)scores_pred = clf.decision_function(X)y_pred = clf.predict(X)# Reshape the prediction values to 0 for valid and 1 for fraudulenty_pred[y_pred == 1] = 0y_pred[y_pred == -1] = 1n_errors = (y_pred != Y).sum()# run classification metrics print('{}:{}'.format(clf_name, n_errors))print(accuracy_score(Y, y_pred ))print(classification_report(Y, y_pred ))

Then it returns the following error:

ValueError: could not convert string to float: '301.48 Change: $0.00'
and it's pointed to  `clf.fit(X)` line.

What have I configured wrong?

We can convert out dataset to numeric data values on the base of their uniqueness and you can also drop un-necessary columns form the dataset.

Here's how you can try that:

df_full = pd.read_excel('input/samp.xlsx', sheet_name=0,)
df_full = df_full[df_full.filter(regex='^(?!Unnamed)').columns]
df_full.drop(['paymentdetails',], 1, inplace=True)
df_full.drop(['timestamp'], 1, inplace=True)
# Handle non numaric data
def handle_non_numaric_data(df_full):columns = df_full.columns.valuesfor column in columns:text_digit_vals = {}def convert_to_int(val):return text_digit_vals[val]if df_full[column].dtype != np.int64 and df_full[column].dtype != np.float64:column_contents = df_full[column].values.tolist()unique_elements = set(column_contents)x = 0for unique in unique_elements:if unique not in text_digit_vals:text_digit_vals[unique] = xx+=1df_full[column] = list(map(convert_to_int, df_full[column]))return df_full