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Data Pre-processing

Data pre-processing is a crucial step in the data analysis process. It involves cleaning, transforming, and preparing raw data into a format suitable for analysis. The quality of the data and the accuracy of the results depend on the pre-processing steps. Here are some common data pre-processing techniques:

  1. Load the data: Import the data from various sources such as CSV files, databases, APIs, etc.
  2. Handle inconsistent data: Check for missing values, duplicate records, and outliers.
  3. Data standardization: Normalize the data to a common scale to make it easier to compare.
  4. Data transformation: Convert categorical data into numerical values using techniques like one-hot encoding.
  5. Introduction of models validation: Split the data into training and testing sets to evaluate the model's performance.

How to save the vars in a file and load them later:

import pickle

# Save the variables to a file
with open('vars.pkl', 'wb') as f:
  pickle.dump([X_train, X_test, y_train, y_test], f)

# Load the variables from a file
with open('vars.pkl', 'rb') as f:
  X_train, X_test, y_train, y_test = pickle.load(f)

Pickle has some security and maintainability issues, so it's better to use joblib for large numpy arrays:

from joblib import dump, load

# Save the variables to a file
dump([X_train, X_test, y_train, y_test], 'vars.joblib')

# Load the variables from a file
X_train, X_test, y_train, y_test = load('vars.joblib')