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CodeSignal

Data Exploration and Baseline Modeling

via CodeSignal

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Overview

In this course, learners will load and inspect a Kaggle dataset, perform exploratory data analysis, preprocess features, and build baseline regression models to establish initial performance benchmarks.

Syllabus

  • Unit 1: Loading and Inspecting a Dataset with Pandas and Scikit-Learn
    • Importing and Previewing the Dataset
    • Inspecting DataFrame Structure
    • Mini-Challenge: Quick Stats Detective
    • Debugging Data Inspection Method Calls
  • Unit 2: Exploratory Data Analysis and Visualization with Matplotlib and Seaborn
    • Identifying Numerical Features with Select Types
    • Identifying Categorical Features with Select Types
    • Customizing Histograms for Focused Analysis
    • Enhancing Histograms with KDE Curves
    • Creating Feature Correlation Heatmaps
    • Creating Masked Correlation Heatmaps
  • Unit 3: Data Cleaning: Handling Missing Values and Encoding Categorical Features
    • Finding Missing Values in Your Data
    • Fixing Numerical Missing Values Consistently
    • Imputing All Numerical Features Efficiently
    • Consistent Categorical Encoding for Models
    • Building a Reusable Preprocessing Function
  • Unit 4: Building and Evaluating Baseline Regression Models
    • Preparing Data for Baseline Models
    • Training Your First Linear Regression Model
    • Building a LightGBM Baseline Model
    • Comparing Models and Analyzing Feature Importance

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