end-to-end-machine-learning.md

• Data Loading
• Data Exploration (EDA)
  • Number of Continuous and Categorical features
    • Checking for Missing values
    • Checking for outliers
    • Univariate analysis
    • Bi-variate analysis
• Data Cleaning
  • Null value treatment
    • Do Nothing
    • Imputation Using (Mean/Median) Values
    • Imputation Using (Most Frequent) or (Zero/Constant) Values
    • Imputation Using k-NN
    • Imputation Using Multivariate Imputation by Chained Equation (MICE) or Iterative Imputation
    • Imputation Using Deep Learning (Datawig)
  • Outlier Treatment
  • Handling Class Imbalance
• Feature Engineering
  • Converting Categorical to numerical features
    • One-hot Encodeing
    • Converting to ordinal feature
• Data Standarization/Normalization
• Feature Selection
  • Correlation
  • Chi-Square Test
  • Feature Importance
• Model Building (using cross validation)
• Hyperparameter Tuning
• Model Evaluation
• Model Saving
• Model Serving