[Python] Scikit-Learn Classifier model returns all zeroes

So, I'm trying to train a RandomForestClassifier Model. However, when I train it, it gives me all zeroes. And, I really can't seem to understand why. The dataset is HUGE (closer to like 75,0000 rows), so, I am a bit lost. Here is the code. :

import pandas as pd
import matplotlib.pyplot as plt
import numpy as np

df = pd.read_csv("train.csv")
X_train = df.iloc[:, 1:-1].values
y_train = df.iloc[:, [-1]].values

df = pd.read_csv("test.csv")
X_test = df.iloc[:, 1:].values

from sklearn.impute import SimpleImputer
imputer = SimpleImputer(missing_values=np.nan, strategy = "most_frequent")
imputer.fit(X_train[:, :])
X_train[:, :] = imputer.transform(X_train[:, :])
X_test[:, :] = imputer.transform(X_test[:, :])

int_features = []
categorical_features = []
for i in range(len(X_train[0])) :
if type(X_train[0]) == int or type(X_train[0]) == float :
int_features.append(i)
elif type(X_train[0]) == str :
categorical_features.append(i)

from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import OneHotEncoder
ct_x = ColumnTransformer(transformers=[('encoder', OneHotEncoder(), categorical_features)], remainder='passthrough')
X_train = np.array(ct_x.fit_transform(X_train))
X_test = np.array(ct_x.transform(X_test))

ct_y = ColumnTransformer(transformers=[('encoder', OneHotEncoder(), [0])], remainder='passthrough', sparse_threshold=0)
y_train = np.array(ct_y.fit_transform(y_train))

from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train[:, int_features] = sc.fit_transform(X_train[:, int_features])
X_test[:, int_features] = sc.transform(X_test[:, int_features])

from sklearn.ensemble import RandomForestClassifier
classifier = RandomForestClassifier(n_estimators=500, max_depth=25, random_state=42)
classifier.fit(X_train, y_train)

y_pred = classifier.predict(X_test)

#print(y_pred)
# Access the OneHotEncoder
ohe = ct_y.named_transformers_['encoder']

# Apply inverse_transform
inverse_transformed_data = ohe.inverse_transform(y_pred)

#print(inverse_transformed_data)
#print(inverse_transformed_data)


Basically, this model has to predict which fertilizer to use based on the data, ie, how good the soil is, etc. I onehotencoded the y_train, because, it containes all the ferilizer names that the model has to predict. However, y_pred just gives me all zeroes, and I don't know why!

Thanks you for any helps or advices you can give.

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