Step-by-Step Salary Prediction with Sklearn: Data Preparation & Model Training(No Pipeline) | Part 1
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In this video, I'll walk you through the process of building a salary prediction model using Python's Scikit-learn library. This is Part 1 of our series, where we'll focus on data preparation and model training without using a pipeline.
📊 What You'll Learn:
How to read and process your data from a CSV file
Splitting the dataset into training and testing sets
Applying label encoding to categorical features
Concatenating transformed features
Training a machine learning model
Evaluating the model's performance
Saving the trained model for future use
🕒 Timestamps:
01:12 Reading the CSV File
03:00 Splitting the Data into Training and Testing Sets
04:57 Label Encoding Categorical Features
07:35 Concatenating Encoded Features
08:14 Training the Model (Fit)
09:20 Evaluating the Model with Accuracy Score
10:57 Saving the Trained Model (Dump)
15:07 Load the Model & Test
Stay tuned for Part 2, where we’ll optimize this process using Sklearn’s powerful Pipeline feature!
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