Data Normalisation in Data Analysis | 🔍 Data Normalization in Python with Pandas 📊
🔍 Data Normalization in Python with Pandas 📊
In this demonstration, I show how to load and preprocess data using Pandas for normalization. Here’s the breakdown:
Loading the Data: Using pd.read_csv() to load the dataset from a CSV file.
Initial Exploration: Examining the first and last 25 rows, data types, statistical summary, and concise info of the DataFrame.
Normalization Techniques: Applying three types of normalization to the 'length' column to ensure consistency:
Simple Feature Scaling: Rescales values between 0 and 1.
Min-Max Scaling: Adjusts data to fit within a defined range (0-1).
Z-score Normalization: Standardizes data by removing the mean and dividing by the standard deviation.
These normalization methods are essential for improving the accuracy and efficiency of machine learning models, ensuring each feature contributes equally to model performance.
📊 Data preprocessing and normalization are crucial steps in preparing datasets for predictive analysis.
#DataScience #MachineLearning #Pandas #DataAnalysis #Normalization #Python #DataPreprocessing
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