Course curriculum

  • 1

    Section 1: Basic Concepts

  • 2

    Section 2: Setting Objectives and Preparing Data

    • Step 1: Goals: First Problem

    • Step 2: Goals: Second problem

    • Step 3: Uploading the dataset

    • Step 4: Exploring and changing columns

    • Step 5: Overview

    • Step 6: Distribution

  • 3

    Section 3: Predicting the Continuous Variable

    • Step 1: First steps

    • Step 2: Analyzing the first result

    • Step 3: Data Leakage

    • Step 4: Prediction without data leakage

    • Step 5: Recalculating

    • Step 6: Filters

    • First Challenge! Predicting the Price for Barcelona

  • 4

    Section 4: Predicting the Categorical Variable

    • Step 1: Neighborhood prediction

    • Second Challenge! Neighborhood prediction without price or taxes

  • 5

    Section 5: Analyzing the Results

    • Step 1: Recapping models

    • Step 2: Drivers

    • Step 3: Correlation does not imply causality

    • Step 4: Personas

    • Step 5: Ideal persona

    • Third Challenge: Creating a prototype of a persona

  • 6

    Section 6: Exporting Predictions

    • Step 1: Test results export

    • Step 2: Exporting results to Web App

    • Step 3: Exporting results to Google Drive

    • Step 4: Predictions by batch

  • 7

    Section 7: Advanced Analysis

    • Step 1: Chosen model

    • Step 2: Training, Validation, and Testing Metrics

    • Step 3: Overfitting

    • Step 4: Loss function

    • Step 5: Advanced metrics

    • Step 6: Regression Metrics

    • Step 7: Classification Metrics

    • Step 8: Advanced Analytics

    • Step 9: Confusion matrix

    • Step 10: Actual vs Predicted Value

  • 8

    Section 8: Other Functionalities

    • Step 1: Data store

    • Step 2: Database connection

  • 9

    Section 9: Conclusion

    • Conclusion