House Price Prediction 




from tensorflow.keras.datasets import boston_housing

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense

# Load dataset
(x_train, y_train), (x_test, y_test) = boston_housing.load_data()

# Normalize data
mean, std = x_train.mean(axis=0), x_train.std(axis=0)
x_train = (x_train - mean) / std
x_test = (x_test - mean) / std

# Define model
model = Sequential([
    Dense(64, activation='relu', input_shape=(x_train.shape[1],)),
    Dense(64, activation='relu'),
    Dense(1)
])

# Compile model
model.compile(optimizer='adam', loss='mse', metrics=['mae'])

# Train model
model.fit(x_train, y_train, epochs=50, batch_size=8, validation_data=(x_test, y_test))

# -------- Prediction Step --------
# Predict on test set
predictions = model.predict(x_test)

# Display first 5 predictions with actual values
for i in range(5):
    print(f"Predicted Price: {predictions[i][0]:.2f}, Actual Price: {y_test[i]:.2f}")




Numbers prediction



import tensorflow as tf from tensorflow.keras.datasets import mnist from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense, Flatten from tensorflow.keras.utils import to_categorical import numpy as np # Load dataset (x_train, y_train), (x_test, y_test) = mnist.load_data() # Normalize inputs x_train, x_test = x_train / 255.0, x_test / 255.0 # One-hot encode outputs y_train, y_test = to_categorical(y_train), to_categorical(y_test) # Build the model model = Sequential([ Flatten(input_shape=(28, 28)), Dense(128, activation='relu'), Dense(10, activation='softmax') ]) # Compile the model model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy']) # Train the model model.fit(x_train, y_train, epochs=5, validation_data=(x_test, y_test)) # Evaluate model loss, accuracy = model.evaluate(x_test, y_test) print(f"\nTest Accuracy: {accuracy:.4f}") # --------------------------- # Prediction on a sample image # --------------------------- # Take the first test image sample_image = x_test[0].reshape(1, 28, 28) # reshape for prediction pred_prob = model.predict(sample_image) # predicted probabilities pred_class = np.argmax(pred_prob) # get class with highest probability print(f"Predicted Label: {pred_class}") print(f"Actual Label: {np.argmax(y_test[0])}")


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