Visualizing relationships: Age of homes and distance to work - SQL Tutorial

Contents

• Introduction Introduction

  • AI and data science: Introduction

    52s
  • What you should know

    2m 45s
  • Using the course exercise files

    2m 41s
• 1. Introduction to Data Science 1. Introduction to Data Science

  • Data science explained

    4m 17s
  • Data or information: What's the difference?

    2m 8s
  • Data's increasing influence

    2m 43s
  • Comparing data roles

    5m 19s
  • Necessary skills for data science

    4m 46s
  • Real-world applications

    5m 46s
  • Challenge: Data

    45s
  • Solution: Data

    1m 12s
• 2. AI, ML, and DL 2. AI, ML, and DL

  • The evolving face of artificial intelligence

    4m 13s
  • Machine learning problem classifications

    4m 29s
  • Machine learning approaches for business scenarios

    3m 26s
  • Challenge: AI

    1m 26s
  • Solution: AI

    38s
• 3. Introduction to Stats and Probability 3. Introduction to Stats and Probability

  • Statistics defined

    2m 38s
  • Statistical mindset

    3m
  • Types of statistics

    2m 45s
  • Descriptive statistics

    7m 30s
  • Inferential statistics (probability)

    5m 44s
  • Python

    3m 58s
  • Challenge: Statistics

    1m 18s
  • Solution: Statistics

    40s
• 4. Predicting House Prices with Linear Regression 4. Predicting House Prices with Linear Regression

  • Project goal

    3m 54s
  • Project steps

    2m 25s
  • Python environment setup

    4m 28s
  • SQL environment setup

    2m 48s
  • Project approach

    3m 24s
• 5. Data Preparation and Exploration 5. Data Preparation and Exploration

  • Importing necessary libraries and dataset overview

    3m 18s
  • Loading the data

    7m 36s
  • Checking the data info

    2m 13s
  • Summary statistics of the dataset

    5m 49s
  • Checking the distribution of the variables

    5m 42s
  • Applying log transformation and re-checking distribution

    3m 6s
  • Challenge: Preparation

    1m 5s
  • Solution: Preparation

    1m 19s
• 6. Data Visualization and Exploration 6. Data Visualization and Exploration

  • Bivariate analysis: Heat-map

    5m 23s
  • Visualizing relationships: Age of homes and distance to work

    3m 52s
  • Visualizing relationships: Highway access and property tax

    4m 9s
  • Checking correlation after removing outliers

    3m 40s
  • Visualizing relationships: Other pairs of variables

    5m 8s
  • Challenge: Visualization

    1m 9s
  • Solution: Visualization

    56s
• 7. Data Preprocessing 7. Data Preprocessing

  • Splitting the dataset into train and test sets

    4m 55s
  • Checking for multicollinearity using VIF

    2m 52s
  • Removing multicollinearity by dropping the tax feature

    2m 58s
  • Challenge: Preprocessing

    1m 15s
  • Solution: Preprocessing

    1m 11s
• 8. Model Building and Evaluation 8. Model Building and Evaluation

  • Creating the linear regression model and model summary: Part 1

    9m 33s
  • Creating the linear regression model and model summary: Part 2

    7m 16s
  • Creating the linear regression model and model summary: Part 3

    5m 33s
  • Dropping insignificant variables and re-creating the model

    7m 57s
  • Checking assumptions for linear regression

    3m 18s
  • Assumption 1: Checking for mean residuals

    2m 47s
  • Assumption 2: Checking homoscedasticity

    3m 13s
  • Assumption 3: Checking linearity

    2m 12s
  • Assumption 4: Checking normality of error terms

    3m 24s
  • Q-Q plot for checking the normality of error terms

    3m 14s
  • Model performance comparison on train and test data

    6m 7s
  • Applying cross-validation and evaluation

    4m 40s
  • Challenge: Model building

    48s
  • Solution: Model building

    1m 16s
• 9. Model Interpretation and Reporting 9. Model Interpretation and Reporting

  • Extracting and creating a DataFrame of coefficients

    5m 53s
  • Writing the linear regression equation and coefficients

    5m 24s
  • Conclusions and business recommendations

    4m 45s
  • Challenge: Interpretation

    55s
  • Solution: Interpretation

    1m 46s
• 10. Final Capstone Project 10. Final Capstone Project

  • Final capstone project details

    1m 9s
  • Final capstone project solution walkthrough

    4m 8s
• Conclusion Conclusion

  • Next steps

    2m 28s