Merge pull request #1 from divyaaa16/NewFeatureEnggBranch

feature engineering

AI-ML Documentation/Data/Feature Engg.txt

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+Feature Engineering: Unleashing the Hidden Potential of Your Data
+
+Feature engineering is the most direct way to improve machine learning model performance. You need to create new features based on existing data so as to better capture underlying patterns and relationships. We're going to take you through steps on how to build new features from existing data and unlock the hidden potential within your data, so model performance can be better maximized.
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+Steps for making new features from existing data for better model performance:
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+Step 1: Understand Your Data
+
+The very first thing before you embark on working on feature engineering is that you need to know your data inside and out. Spend quality time researching your dataset, try out the distribution of values, the relationships, correlations, and look up for missing values and outliers in your dataset. All these will guide you in revealing the strengths and weaknesses about your data in order to facilitate each of your decisions based on feature engineering and your use of this data in optimal usage.
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+Key Takeaway
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+Analyze types of variables: categorical, numerical, text, etc.
+Study value distribution: mean, median, mode, standard deviation, etc.
+Find out if there are correlations between the variables
+Detect missing values and outliers
+
+Step 2: Choose the Best Features
+
+Based on your understanding of the data, it is time to choose the most relevant features that are going to add up to the performance of your model. Avoiding the curse of dimensionality and overfitting at this step is a priority. Techniques include correlation analysis, mutual information, recursive feature elimination, and permutation importance in order to figure out the best features.
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+Key Techniques:
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+.Correlation analysis: Find the high correlation of features with the target variable
+Mutual information : Feature with the highest mutual information with the target variable is found. Recursive feature elimination (RFE): The least important feature is eliminated at each step and the process continues. Permutation importance : The contribution of every feature is calculated by permuting the values
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+Transformation and Refining Step 3
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+Transform and polish the current features to enhance the quality and relevance of them. This may include normalization or scaling the features to a common range, handling the categorical variables, transforming the text data into numerical values, and applying logarithmic or square root transformations for stabilization of variance.
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+Important Transformations:
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+Normalization: Scaling features to a common range, for example, between 0 and 1.
+Scaling: Scaling the features to a particular range, for example, -1 to 1.
+.Handling categorical variables: One-hot encoding, label encoding, etc.
+.Text data conversion: Word embeddings, TF-IDF, etc.
+Logarithmic or square root transformations: Stabilizing variance and performance of a model
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+Step 4: Extract New Features
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+Get new features using dimensionality reduction, feature aggregation, feature splitting, and text feature extraction from existing ones. This involves high creativity, with proper knowledge of the data on hand, for you wish to find novel patterns or relationships that improve the performance of the model.
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+Key Techniques:
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+.Dimensionality Reduction: PCA, t-SNE, autoencoders, etc.
+Feature Aggregation: Gather several features into one feature (mean, sum, count, etc.).
+Feature splitting: Taking the values of a single feature and creating multiple features of those (for example: date features that can split out into day, month and year).
+.Text feature extraction: This includes all feature types which have been described. The tasks include sentiment analysis, entity recognition, and topic modeling, among many others.
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+Step 5: Generate New Features ex-novo
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+Develop new features from scratch, based on your domain knowledge and creativity. This step depends a lot on understanding the problem and data since you would be trying to develop features that have been fashioned to fit certain patterns and relationships in the underlying data. Examples include ratios or proportions between features, interaction terms between features, and extracting features from outside data sources.
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+Key Examples
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+.Calculating ratios or proportions between features
+.Creating interaction terms between features
+Feature extraction from external data sources (e.g., weather, economic indicators)
+Step 6: Evaluation and Refining
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+Evaluate your new features' performance based on metrics such as correlation with the target variable, mutual information, permutation importance, and model performance metrics. This step will identify the most effective features, refine your feature engineering process, and ensure you make the most of your data.
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+Key Metrics:
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+.Correlation with the target variable
+.Mutual information
+.Permutation importance
+Accuracy or precision and recall of model performance
+Step 7: Choose and Train
+Choose the best features working with the model and let the model be trained from updated features. Keep track of the model's performance; keep on updating the selected features to achieve the result that is as good as achievable.
+Key Activities:
+•Choose best features
+ •Train the model using an updated set of features.
+ •Track the model performance and keep updating the chosen set of features
+Step 8: Evaluate and Iterate
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+Evaluate the performance of your new model with different metrics and techniques and refine the feature engineering process by iterating through the previous steps. Retrain the model with the updated feature set and continue iterating until you get the desired level of performance.
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+Key Activities:
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+.Model performance evaluation using various metrics and techniques
+.Refine the feature engineering process by iterating through the previous steps
+.Retrain the model with the updated feature set
+Continue iterating until you reach the desired level of performance
+Following these steps, you will be able to unlock the hidden potential in your data, creating new features that improve model performance and business success.
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+Don't forget the process of feature engineering itself to be iterative in the sense that you do have to experiment and try other things. With enough practice and patience, you'd master feature engineering to unlock all this potential from your data toward business success.