i want know Accuracy and confusion matrix for my custom object detection dataset. getting value error

Question

from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
import pandas as pd
import matplotlib.pyplot as plt # plotting
import numpy as np # dense matrices
from scipy.sparse import csr_matrix # sparse matrices
%matplotlib inline

df = pd.read_csv('train_labels.csv')
df.head(1728)
df['class'].value_counts()
#Defining features and target variable
y = df['class'] #target variable we want to predict
X = df.drop(columns = ['class']) #set of required features, in this case all
#Splitting the data into train and test set
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)
#Predicting using Logistic Regression for Binary classification
LR = LogisticRegression()
LR.fit(X_train,y_train) #fitting the model
y_pred = LR.predict(X_test) #prediction

ValueError: could not convert string to float: '889.jpg'
for this line
LR.fit(X_train,y_train) #fitting the model

Answer 1

Logistic regression requires numeric input. You can't use strings in your training dataset

Answer 2

Artem Khaiet any way to solve it??

Answer 3

Anik Datta use a different model...