ML Process - dudycooly/1235 GitHub Wiki

Step 1: Define your problem

• What is the problem? Describe the problem informally and formally and list assumptions and similar problems.
• Why does the problem need to be solve? List your motivation for solving the problem, the benefits a solution provides and how the solution will be used.
• How would I solve the problem? Describe how the problem would be solved manually to flush domain knowledge. Read More>>

Step 2: Prepare your data.

• How to Prepare Data For Machine Learning
• How to Identify Outliers in your Data
• Improve Model Accuracy with Data Pre-Processing
• Discover Feature Engineering
• An Introduction to Feature Selection
• Tactics to Combat Imbalanced Classes in Your Machine Learning Dataset
• Data Leakage in Machine Learning

Step 3: Spot-check algorithms.

• How to Evaluate Machine Learning Algorithms
• Why you should be Spot-Checking Algorithms on your Machine Learning Problems
• How To Choose The Right Test Options When Evaluating Machine Learning Algorithms
• A Data-Driven Approach to Choosing Machine Learning Algorithms

Step 4: Improve results.

• How to Improve Machine Learning Results
• Machine Learning Performance Improvement Cheat Sheet
• How To Improve Deep Learning Performance

Step 5: Present results.

• How to Use Machine Learning Results
• How to Train a Final Machine Learning Model
• How To Deploy Your Predictive Model To Production

Source: https://machinelearningmastery.com/start-here/#process