ML Basics with Keras in Tensorflow: Regression: Inspect the data

To inspect the data in a regression problem with Keras in TensorFlow, you can use the following steps:

• Import the data into a Pandas DataFrame.
• Clean the data by removing any rows with missing values.
• Split the data into a training set and a test set.
• Inspect the data by looking at the following:
• The distribution of the data
• The correlation between the features
• The outliers in the data

Here is an example of how to inspect the data in a regression problem with Keras in TensorFlow:

import pandas as pd

# Import the data

data = pd.read_csv('data.csv')

# Clean the data

data = data.dropna()

# Split the data into a training set and a test set

train_data = data.sample(frac=0.8)

test_data = data.drop(train_data.index)

# Inspect the data

print(train_data.describe())

print(train_data.corr())

print(train_data.boxplot())

The output of the code above will show you the distribution of the data, the correlation between the features, and the outliers in the data. This information can be used to choose the right model for your regression problem and to tune the hyperparameters of the model.

Here are some additional tips for inspecting the data in a regression problem with Keras in TensorFlow:

• Use a variety of visualization tools to inspect the data. This can help you to identify patterns in the data that may not be obvious from looking at the raw data.
• Use statistical tests to measure the significance of the relationships between the features and the target variable. This can help you to determine which features are most important for predicting the target variable.
• Experiment with different models and hyperparameters to find the best model for your data. This can help you to improve the accuracy of your predictions