What are the key steps in preparing your data for machine learning?

Data preparation is a crucial step in the machine learning pipeline. It involves several key steps. - Data Collection: Gather data relevant to the problem. - Data Cleaning: Handle missing values, outliers, and remove duplicates. - Data Transformation: Normalize or scale data to bring all features to the same level of magnitude. - Feature Engineering: Create new features from existing ones to better represent the underlying patterns. - Data Splitting: Divide data into training, validation, and test sets to evaluate model performance. If you have any additional thoughts or contributions, please reply to this comment. I always appreciate and look forward to hearing more from you. Thank you!

Key steps in preparing data for machine learning: Collect relevant data. Clean data by handling missing values and outliers. Select relevant features. Scale numerical features. Transform categorical variables. Split data into training and testing sets. Engineer features for better model performance. Address class imbalance if present. Handle skewness and distribution. Augment data if needed. These steps optimize data for training accurate machine learning models.

1. Gather Relevant Data and consider bias. (The data should be sufficient in quantity and quality). 2. Data Cleaning - Identify and Handle Missing Values, Identify and Address Inconsistent Formatting, Identify and Correct Errors and Handle outliers. 3. Data Transformation and Feature Selection/Engineering- involves Feature Scaling, Encoding Categorical Data, Dimensionality Reduction (sometimes), Identifying relevant features and Creating new features. 4. Data Splitting for Training and Testing - Typically, use a 70-80% training set, 10-15% validation set (for hyperparameter tuning), and 10-15% test set (for final model evaluation).

You look at how much data you have, what it looks like, and if there are any strange things in it. You also draw pictures of the data to see if there are any patterns or interesting things hidden inside. Exploring data helps you decide which parts are important for teaching the computer and how to make sure it learns the right things.

Data encoding is really one of the important step while preprocessing of the data. Sometimes your data may contain fields which are textual in nature which becomes an issue for your model performance (especially when they occupy more than 50% of your fields). For proper analysis and better performance of your model you need to convert these categorical fields into numerical one. The best approaches that could be used are one hot encoding and label encoder. Once your categorical fields are converted to the numerical data, one can consider dropping the categorical fields from your dataset which donot contribute to your analysis.