How can Tensorflow be used to load the csv data from abalone dataset?

The abalone dataset can be downloaded by using the google API that stores this dataset. The ‘read_csv’ method present in the Pandas library is used to read the data from the API into a CSV file. The names of the features are also specified explicitly.

Read More: What is TensorFlow and how Keras work with TensorFlow to create Neural Networks?

We will be using the abalone dataset, which contains a set of measurements of abalone. Abalone is a type of sea snail. The goal is to predict the age based on other measurements.

We are using the Google Colaboratory to run the below code. Google Colab or Colaboratory helps run Python code over the browser and requires zero configuration and free access to GPUs (Graphical Processing Units). Colaboratory has been built on top of Jupyter Notebook.

import pandas as pd
import numpy as np

print("The below line makes it easier to read NumPy values")
np.set_printoptions(precision=3, suppress=True)

import tensorflow as tf
from tensorflow.keras import layers
from tensorflow.keras.layers.experimental import preprocessing

print("Reading the csv data")
abalone_train = pd.read_csv("",
      names=["Length", "Diameter", "Height", "Whole weight", "Shucked weight","Viscera weight", "Shell weight", "Age"])

Code credit:


The below line makes it easier to read NumPy values
Reading the csv data


  • The required packages are downloaded into the environment.
  • The CSV data is read using the 'read_csv' method.
  • All the features in the dataset need to be treated identically.
  • Once this is done, the features are wrapped into a single NumPy array.