DATA PREPROCESSING

MIND MAP

Data preprocessing is a crucial step in any data analysis or machine learning pipeline. It involves preparing and transforming raw data into a clean and usable format before applying any model or analysis.

Data Collection

Data collection involves gathering information for analysis to understand trends, patterns, or insights.

DATA CLEANING

Before building a machine learning model, it’s crucial to preprocess the data to ensure quality. Data preprocessing involves cleaning the data to handle missing or incorrect values, and transforming it to a format that can be understood by the machine learning algorithms.

Check data size

Check column names

Drop unimportant features from the DataFrames

Remove duplicates from the DataFrames

Before

After

DATA TRANSFORMATION

One-Hot Encoding

After the data was cleaned, categorical features were transformed into numerical representations using one-hot encoding. One-hot encoding is a technique used to convert categorical variables into a numerical format that machine learning algorithms can understand.

Code

Result

technical_skills = df['Technical Skills'].str.get_dummies(sep=',')

One-hot encode 'Technical Skills' column

soft_skills = df['Soft Skills'].str.get_dummies(sep=',')

One-hot encode 'Soft Skills' column

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