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CodeSignal

Introduction to Predictive Modeling

via CodeSignal

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Overview

Initiate your understanding of predictive modeling by exploring the fundamental workings and purposes of these models. Gain insights into how predictive models can guide decision-making across industries and sectors.

Syllabus

  • Unit 1: Demystifying Predictive Modeling with the California Housing Dataset
    • Exploring the Structure of the California Housing Dataset
    • Scatter Plot Insights: Latitude and Median Value
    • Exploring the Impact of Longitude on Housing Prices
    • Scatter Plot Exploration
    • California Housing Scatter Plot Creation
  • Unit 2: Linear Regression: From Basics to Predictive Modeling
    • Predicting Tree Height with Linear Regression
    • Understanding the Impact of Data Changes on Regression
    • Regression Line Prediction Error
    • Predicting with Linear Regression
    • Charting the Regression Universe
  • Unit 3: Fitting a Linear Regression Model to the Housing Dataset with Sklearn
    • Visualizing Linear Regression in the Housing Market
    • Fixing Dimensionality for Linear Regression Model
    • Predicting House Values with Different Incomes
    • Creating and Predicting with a Linear Regression Model
    • Implementing Linear Regression to Make a Prediction
  • Unit 4: Evaluating a Prediction Model with MSE
    • Evaluating House Prices Predictions with MSE
    • Linear Regression Model Evaluation Error
    • Evaluating Predictive Models: Calculating MSE
    • Calculating Mean Square Error for Model Evaluation
    • Evaluating the Predictive Model with MSE

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