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CodeSignal

Preparing Your Data and Setting a BaseLine

via CodeSignal

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Overview

In this course, you'll go through the essential steps for loading, exploring, and preprocessing the data. You'll handle missing values, encode categorical variables, and train a baseline model to establish a performance benchmark.

Syllabus

  • Unit 1: Loading Data with Pandas: A Beginner's Guide
    • Loading and Exploring Customer Data
    • Exploring Categorical Data with Pandas
    • Understanding DataFrame Size and Structure
  • Unit 2: Data Exploration: Visualizing and Analyzing Your Dataset
    • Visualizing Credit Score Distribution
    • Visualizing Device Usage with Countplots
    • Visualizing Feature Relationships with Heatmaps
  • Unit 3: Handling Missing Values and Encoding in Data Preprocessing
    • Filling Numerical Gaps with Mean
    • Filling Activity Gaps with Zeros
    • Filling Categorical Gaps with Unknown
    • Filling Device Gaps with Mode
    • One-Hot Encoding Categorical Variables
  • Unit 4: Training a Baseline Model
    • Preparing Data for Model Training
    • Training and Interpreting Logistic Regression
    • Evaluating Model Performance on Test Data
    • Analyzing Prediction Class Distribution

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