Stochastic Gradient Descent on Linear Regression

  • Guided Project
  • 0.4 hours
  • Intermediate
  • Python

Practice building a stochastic gradient descent linear regression model in Python to predict gym crowdedness.

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Overview

In this project, you'll assume the role of a data scientist who wants to predict the least crowded times at the gym. Using a dataset with historical records of gym attendance, you'll build a stochastic gradient descent linear regression model in Python. You'll load and explore the data using pandas, prepare it for modeling, train a SGDRegressor, evaluate the model's performance, and visualize the results. This project allows you to apply key machine learning concepts like gradient descent while developing your data science portfolio. Objective: Build a stochastic gradient descent model in Python to predict gym crowdedness and determine the optimal time to avoid crowds.

What You'll Learn

  • ✓ Explore a new dataset
  • ✓ Prepare the data to build a model using SGDRegression
  • ✓ Measure the efficiency of the model

Before You Start

  • ✓ Writing Python code to implement basic algorithms
  • ✓ Understanding fundamental machine learning concepts and algorithms
  • ✓ Exploring and preparing datasets to build machine learning models
  • ✓ Evaluating machine learning model performance

Project Steps

9 steps

  1. 1 Stochastic Gradient Descent on Linear Regression
  2. 2 Import Libraries and Load the Data
  3. 3 EDA and Cleaning the Data
  4. 4 Preparing to Build Our Model
  5. 5 Measure the Performance of the Model
  6. 6 Visualize the Results
  7. 7 Summarize Your Results
  8. 8 Congratulations, you did it!
  9. 9 Next Steps
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