creating a machine learning model
Data Gathering and Preparation
- Identify the data sources that are relevant to the problem or objective of the machine learning model.
- Collect the necessary data from these sources to be used for training and testing the model.
- Identify and handle any missing values in the data by either removing them or filling them in with appropriate values.
- Identify and handle any outliers in the data by either removing them or applying appropriate transformations.
- Identify and handle any inconsistencies in the data by standardizing or normalizing the data.
- Divide the data into separate subsets for training, validation, and testing the machine learning model.
- Typically, the data is split into a training set (used to train the model), a validation set (used to tune the model hyperparameters), and a test set (used to evaluate the final model performance).
Exploratory Data Analysis (EDA)
- Calculate mean, median, mode, minimum, maximum, standard deviation, and quartiles
- Count the number of missing values
- Check for outliers
- Create scatter plots to visualize relationships between variables
- Generate histograms to understand the distribution of variables
- Plot correlation matrices to identify the strength and direction of relationships
- Select relevant features based on domain knowledge and correlation analysis
- Transform variables using logarithmic, exponential, or polynomial functions
- Create new features by combining existing variables or extracting information from text or timestamps
- Calculate correlation coefficients between each feature and the target variable
- Visualize the relationship using scatter plots or correlation matrices
- Identify features with high correlation to determine their predictive power
Use in Manifestly