Fine-Tuning Inputs: Data Preprocessing Techniques for Neural Networks

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Processing data before feeding it into a neural network is a crucial step in the machine learning pipeline. The way you preprocess data depends on the type of data and the architecture of the neural network you’re using. Here, I’ll provide a general overview of the common steps involved in data preprocessing for neural networks, considering various types of data:

1. Data Cleaning:

• Handle Missing Values: Identify and handle missing values appropriately. You can either remove the rows or columns containing missing data or impute the missing values using methods like mean, median, or interpolation.

import pandas as pd

# Assuming 'df' is your DataFrame
# Drop rows with missing values
df = df.dropna()

# Alternatively, fill missing values with the mean
df = df.fillna(df.mean())

2. Data Normalization/Standardization:

• Normalization: Scale the numerical features to a standard range, often between 0 and 1. This is particularly important for algorithms sensitive to the scale of input features, such as neural networks.

from sklearn.preprocessing import MinMaxScaler, StandardScaler

# Min-Max Normalization
min_max_scaler = MinMaxScaler()
df_normalized =…