Hello, I was following the tutorial on Deep Learning for Computer Vision with Python and TensorFlow – Complete Course on FreeCodeCamp and while I’m doing the project Car Price Prediction, I just encountered an error and tried to solve it but failed.

Here is the code

```
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
import pandas as pd
import seaborn as sns
from keras.layers import Normalization, Dense, InputLayer
from keras.losses import MeanSquaredError, Huber, MeanAbsoluteError
from keras.optimizers import Adam
from keras.metrics import RootMeanSquaredError
data = pd.read_csv("/Users/atatekeli/PycharmProjects/CompVision/comp-vision-projects/tensorflow-comp-vision/Car Price Prediction/carprice.csv")
print(data.head())
print(data.shape)
print(data.describe())
sns.pairplot(data[['years', 'km', 'rating', 'condition', 'economy', 'top speed', 'hp', 'torque', 'current price']], diag_kind='kde')
tensor_data = tf.constant(data)
tensor_data = tf.cast(tensor_data, tf.float32)
print(tensor_data)
tensor_data = tf.random.shuffle(tensor_data)
print(tensor_data[:5])
X = tensor_data[:,3:-1]
print(X[:5])
y = tensor_data[:,-1]
print(y[:5].shape)
y = tf.expand_dims(y, axis = -1)
print(y[:5])
normalizer = Normalization(axis = -1, mean = 5, variance = 4)
x_normalized = tf.constant([[3,4,5,6,7],
[4,5,6,7,8]])
normalizer(x_normalized)
normalizer = Normalization()
x_normalized = tf.constant([[3,4,5,6,7],
[4,10,6,7,8],
[32,1,56,3,5]])
normalizer.adapt(x_normalized)
normalizer(x_normalized)
print(X.shape)
TRAIN_RATIO = 0.8
VAL_RATIO = 0.1
TEST_RATIO = 0.1
DATASET_SIZE = len(X)
X_train = X[:int(DATASET_SIZE*TRAIN_RATIO)]
y_train = y[:int(DATASET_SIZE*TRAIN_RATIO)]
print(X_train.shape)
print(y_train.shape)
train_dataset = tf.data.Dataset.from_tensor_slices((X_train, y_train))
train_dataset = train_dataset.shuffle(buffer_size = 8, reshuffle_each_iteration = True).batch(32).prefetch(tf.data.AUTOTUNE)
for x,y in train_dataset:
print(x,y)
break
X_val = X[int(DATASET_SIZE*TRAIN_RATIO):int(DATASET_SIZE*(TRAIN_RATIO+VAL_RATIO))]
y_val = y[int(DATASET_SIZE*TRAIN_RATIO):int(DATASET_SIZE*(TRAIN_RATIO+VAL_RATIO))]
print(X_val.shape)
print(y_val.shape)
val_dataset = tf.data.Dataset.from_tensor_slices((X_val, y_val))
val_dataset = train_dataset.shuffle(buffer_size = 8, reshuffle_each_iteration = True).batch(32).prefetch(tf.data.AUTOTUNE)
X_test = X[int(DATASET_SIZE*(TRAIN_RATIO+VAL_RATIO)):]
y_test = y[int(DATASET_SIZE*(TRAIN_RATIO+VAL_RATIO)):]
print(X_test.shape)
print(y_test.shape)
test_dataset = tf.data.Dataset.from_tensor_slices((X_test, y_test))
test_dataset = train_dataset.shuffle(buffer_size = 8, reshuffle_each_iteration = True).batch(32).prefetch(tf.data.AUTOTUNE)
normalizer = Normalization()
normalizer.adapt(X_train)
print(normalizer(X)[:5])
print(X[:5])
"""## **Model Creation and Training**"""
model = tf.keras.Sequential([
InputLayer(input_shape = (8,)),
normalizer,
Dense(128, activation = "relu"),
Dense(128, activation = "relu"),
Dense(128, activation = "relu"),
Dense(1),
])
print(model.summary())
tf.keras.utils.plot_model(model, to_file = "model.png", show_shapes=True)
model.compile(optimizer = Adam(learning_rate = 0.1),
loss = MeanAbsoluteError(),
metrics = RootMeanSquaredError())
history = model.fit(train_dataset, validation_data=val_dataset, epochs = 100, verbose = 1)
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'val_loss'])
plt.show()
plt.plot(history.history['root_mean_squared_error'])
plt.plot(history.history['val_root_mean_squared_error'])
plt.title('model performance')
plt.ylabel('rmse')
plt.xlabel('epoch')
plt.legend(['train', 'val'])
plt.show()
"""## **Model Evaluation and Testing**"""
model.evaluate(X_test,y_test)
print(X_test.shape)
model.predict(tf.expand_dims(X_test[0], axis = 0 ))
print(y_test[0])
y_true = list(y_test[:,0].numpy())
y_pred = list(model.predict(X_test)[:,0])
print(y_pred)
ind = np.arange(100)
plt.figure(figsize=(40,20))
width = 0.1
plt.bar(ind, y_pred, width, label='Predicted Car Price')
plt.bar(ind + width, y_true, width, label='Actual Car Price')
plt.xlabel('Actual vs Predicted Prices')
plt.ylabel('Car Price Prices')
plt.show()
```