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[Activity] Multiple Regression, and Predicting Car Prices 

  • [Activity] Multiple Regression, and Predicting Car Prices 

[Activity] Multiple Regression, and Predicting Car Prices 

  • November 1, 2019
  • 2

Back to: Machine Learning, Data Science, and Deep Learning with Python

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[Activity] Polynomial Regression
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Multi-Level Models
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Article Comments

  1. Yasir Tharayil

    March 2, 2021 4:23 pm Log in to Reply

    Dont we have the constant(B0) value in summary (along with other coeficients)?
    if yes,which one it is?

    • Frank Kane

      March 2, 2021 5:29 pm Log in to Reply

      Great question. The OLS module in statsmodel sets B0 to 0, so it’s not used in its results.
      There are exceptions though; if you read through its docs at https://www.statsmodels.org/dev/generated/statsmodels.regression.linear_model.OLS.html you can get all the details.

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Lessons

  • Getting Started
    • Introduction
    • Installation: Getting Started
    • [Activity] WINDOWS: Installing and Using Anaconda & Course Materials
    • [Activity] MAC: Installing and Using Anaconda & Course Materials
    • [Activity] LINUX: Installing and Using Anaconda & Course Materials
    • Python Basics, Part 1 [Optional]
    • [Activity] Python Basics, Part 2 [Optional]
    • [Activity] Python Basics, Part 3 [Optional]
    • [Activity] Python Basics, Part 4 [Optional]
    • Introducing the Pandas Library [Optional]
  • Statistics and Probability Refresher, and Python Practice
    • Types of Data
    • Mean,  Median, Mode
    • [Activity] Using mean, median, and mode in Python
    • [Activity] Variation and Standard Deviation
    • Probability Density Function; Probability Mass Function
    • Common Data Distributions
    • [Activity] Percentiles and Moments
    • [Activity] A Crash Course in matplotlib
    • [Activity] Advanced Visualization with Seaborn
    • [Activity] Covariance and Correlation
    • [Exercise] Conditional Probability
    • Exercise Solution: Conditional Probability
    • Bayes’ Theorem
  • Predictive Models
    • [Activity] Linear Regression
    • [Activity] Polynomial Regression
    • [Activity] Multiple Regression, and Predicting Car Prices 
    • Multi-Level Models
  • Machine Learning with Python
    • Supervised vs. Unsupervised Learning, and Train/Test
    • [Activity] Using Train/Test to Prevent Overfitting
    • Bayesian Methods: Concepts
    • [Activity] Implementing a Spam Classifier with Naive Bayes
    • K-Means Clustering
    • [Activity] Clustering People Based on Income and Age
    • Measuring Entropy
    • [Activity] WINDOWS: Installing GraphViz
    • [Activity] MAC: Installing GraphViz
    • [Activity] LINUX: Installing GraphViz
    • Decision Trees: Concepts
    • [Activity] Decision Trees: Predicting Hiring Decisions
    • Ensemble Learning
    • [Activity] XGBoost
    • Support Vector Machines (SVM) Overview
    • [Activity] Using SVM to Cluster People using scikit-learn
  • Recommender Systems
    • User-Based Collaborative Filtering
    • Item-Based Collaborative Filtering
    • [Activity] Finding Movie Similarities
    • [Activity] Improving the Results of Movie Similarities
    • [Activity] Making Movie Recommendations to People
    • [Exercise] Improve the Recommender’s Results
  • More Data Mining and Machine Learning Techniques
    • K-Nearest-Neighbors: Concepts
    • [Activity] Using KNN to Predict a Rating for a Movie
    • Dimensionality Reduction; Principal Component Analysis (PCA)
    • [Activity] PCA Example with the Iris Data Set
    • Data Warehousing Overview: ETL and ELT
    • Reinforcement Learning
    • [Activity] Reinforcement Learning and Q-Learning with Gym
    • Understanding a Confusion Matrix
    • Measuring Classifiers (Precision, Recall, F1, AUC, ROC)
  • Dealing with Real-World Data
    • Bias / Variance Tradeoff
    • [Activity] K-Fold Cross-Validation
    • Data Cleaning and Normalization
    • [Activity] Cleaning Web Log Data
    • Normalizing Numerical Data
    • [Activity] Detecting Outliers
    • Feature Engineering and the Curse of Dimensionality
    • Imputation Techniques for Missing Data
    • Handling Unbalanced Data: Oversampling, Undersampling, and SMOTE
    • Binning, Transforming, Encoding, Scaling, and Shuffling
  • Apache Spark: Machine Learning on Big Data
    • Warning about Java 11!
    • Spark Installation Notes for MacOS and Linux Users
    • [Activity] Installing Spark – Part 1
    • [Activity] Installing Spark – Part 2
    • Spark Introduction
    • Spark and the Resilient Distributed Dataset
    • Introducing MLLib
    • Introduction to Decision Trees in Spark
    • [Activity] K-Means Clustering in Spark
    • TF / IDF
    • [Activity] Searching Wikipedia with Spark
    • [Activity] Using the Spark 2.x DataFrame API for MLLib
  • Experimental Design / ML in the Real World
    • Deploying Models to Real-Time Systems
    • A/B Testing Concepts
    • T-Tests and P-Values
    • [Activity] Hands-on With T-Tests
    • Determining How Long to Run an Experiment
    • A/B Test Gotchas
  • Deep Learning and Neural Networks
    • Deep Learning Pre-Requisites
    • The History of Artificial Neural Networks
    • [Activity] Deep Learning in the Tensorflow Playground
    • Deep Learning Details
    • Introducing Tensorflow
    • [Activity] Using Tensorflow, Part 1
    • [Activity] Using Tensorflow, Part 2
    • [Activity] Introducing Keras
    • [Activity] Using Keras to Predict Political Affiliations
    • Convolutional Neural Networks (CNN’s)
    • [Activity] Using CNN’s for Handwriting Recognition
    • Recurrent Neural Networks (RNN’s)
    • [Activity] Using a RNN for Sentiment Analysis
    • [Activity] Transfer Learning
    • Tuning Neural Networks: Learning Rate and Batch Size Hyperparameters
    • Deep Learning Regularization with Dropout and Early Stopping
    • The Ethics of Deep Learning
    • Learning More about Deep Learning
  • Final Project
    • Your Final Project Assignment
    • Final Project Review
  • You Made It!
    • More to Explore
    • Continue Your Learning!
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