Step1 : Problem statement :

  • What is the Objective?
  • What is the expected output? for example: Binary variable (example: 0 = “first prediction”, 1 = “second prediction”).

Step2 : Data Collection :

  • Ensure all the data is collected from all the possible sources, data can be strcuctured, unstructured, semi-structured or all.

Step3 : Data Preprocessing :

  • 3.1 Data Cleaning:
    • Handling missing values :
      • It depends on the type of data. For example: For numerical type column one could choose mean/median/mode depending on the requirements.
      • Or if it is string type data, one can fill it with N/A, unknown etc or data can be dropped.
    • Removing duplicates: Ensure that there are no duplicates in the dataset, to avoid skewness of the results.
    • This preprocessing usually depends on the requirements.
  • 3.2 Data Transformation
    • Feature Engineering : This step involves (if needed) create new features that could help in the prediction.
    • Normalization/Standardization: Scale the data if you’re using models that are sensitive to the magnitude of input values.

  • 3.3 Handling Dates(if needed) : Data parsing should be correct, consistent and extract useful information from dates.

  • 3.4 Encoding Categorical Data: Convert categorical variables into a form that can be provided to ML models by using one-hot encoding, label encoding, or using embedding layers for deep learning models.

    • 3.4.1 : Label Encoding : It is usually prefered when the categorical feature is ordinal (the categories have a meaningful order or rank). 
        from sklearn.preprocessing import LabelEncoder
  encoder = LabelEncoder()
  # encoder.fit_transform(...)
    • 3.4.2 : One-Hot Encoding with sklearn : It is usually preferred when you are implementing a pipeline or need to handle unseen data during model deployment. Similar to pd.get_dummies() 
            from sklearn.preprocessing import OneHotEncoder
      encoder = OneHotEncoder(drop='first', sparse=False)
      # encoder.fit_transform(...)
    • 3.4.3 : Hashing : Preferred when number of unique categories is large and dimentionality reduction is needed. 
            from sklearn.feature_extraction import FeatureHasher
      hasher = FeatureHasher(n_features=6, input_type='string')
      # hasher.transform(.....)
    • 3.4.4 : Category Encoders : Advanced encoding techniques for categorical variables 
            import category_encoders as ce
      encoder = ce.BinaryEncoder(cols=['month', 'day_of_week'])
      # encoder.fit_transform(df)

Step4 : Feature Selection :

  • 4.1 Correlation Analysis: Remove highly correlated features to reduce multicollinearity.
  • 4.2 Importance Metrics: Utilize model-based metrics like feature importance or etc.

Step5 : Data Partitioning :

Split the data into training, validation, and test sets.

Step6 : Developing a Baseline Model :

Start with a simple model to establish a baseline performance for example Logistic regression or a basic decision tree.

Step7 : Model Training and Evaluation :

  • Train your model on the training set and evaluate it using the validation set.
  • Adjust parameters, as needed, and consider upgrading model based on the parameters by fine-tuninig or model enhancement.

Step8 : Final Evaluation and Deployment!!!

Note: The key in machine learning, particularly for predictions like this, is iteration. So, Steps(3-8) can be repeated until suitable performance is achieved. And, these steps depends on the problem statements.

.ipynb Notebook