ML model is predicting the same output for different inputs

[na2209]

My ML model is predicting the same output for different inputs can someone help me?
Here is the code

# Import necessary libraries
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score

# Step 1: Load the dataset
data = pd.read_excel('C:\\Users\\hp\\Desktop\\SugarcaneDataset.xlsx')  # Adjust the file path as needed

# Step 2: Split the dataset into features (X) and target variable (y)
X = data.drop(columns=['Brix'])  # Features
y = data['Brix']  # Target variable

# Step 3: Split the dataset into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Step 4: Initialize and train the Random Forest Regression model
rf_regressor = RandomForestRegressor(n_estimators=100, random_state=42)  # Adjust hyperparameters as needed
rf_regressor.fit(X_train, y_train)

# Step 5: Make predictions on the testing set
y_pred = rf_regressor.predict(X_test)

# Step 6: Evaluate the model's performance
mse = mean_squared_error(y_test, y_pred)
mae = mean_absolute_error(y_test, y_pred)
r2 = r2_score(y_test, y_pred)

print("Mean Squared Error:", mse)
print("Mean Absolute Error:", mae)
print("R-squared:", r2)

Step 7: Optionally, deploy the model and document the process

Now when i want to get predicted value of diff samples it gives me the same for both even these both are different:
1)

predicted_brix = rf_regressor.predict([  
[
530.279,	162.9097,	74.48293,	41.56035,	29.57368,	17.8308
]])
print("Predicted Brix value:", predicted_brix)
Predicted Brix value: [17.774]

predicted_brix = rf_regressor.predict([  
[
613.5688,	172.6195,	82.26472,	43.53942,	31.61325,	19.01953

]])
print("Predicted Brix value:", predicted_brix)
Predicted Brix value: [17.774]

Why this is happening?

[mauriceoboya]

Your model might be overfitting the training data, capturing noise rather than underlying patterns. This can lead to poor generalization to new data. You could try to; Check for multicollinearity among features, normalizing your features if they are on different scales or even Experiment with different machine learning algorithms.

[na2209]

Still facing the same issue

[sachinprasadhs]

This repo is for reporting the bugs for the Keras framework only.
From your code, it does not look like you're using Keras.
For support related questions you can post in Stack Overflow community.

[google-ml-butler]

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