Project 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.
Key skill required
To complete this project, it's recommended to build these foundational skills in Python
- 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
Projects steps
Step 1: Stochastic Gradient Descent on Linear Regression
Step 2: Import Libraries and Load the Data
Step 3: EDA and Cleaning the Data
Step 4: Preparing to Build Our Model
Step 5: Measure the Performance of the Model
Step 6: Visualize the Results
Step 7: Summarize Your Results
Step 8: Congratulations, you did it!
Step 9: Next Steps
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