Ml phases
graph LR;
A[Machine Learning Phases] --> B[Data Handling]
B --> B1[Data Collection]
B1 --> B_EXP{{"Gathering raw data from various sources"}}
B1 --> B_NOTE["Sources include databases, APIs, user input"]
B --> B2[Data Preparation]
B2 --> B2_EXP{{"Cleaning and preprocessing data"}}
B2 --> B2_NOTE["Includes handling missing values, normalization"]
A --> C[Feature Engineering]
C --> C1[Feature Engineering]
C1 --> C1_EXP{{"Creating and modifying features"}}
C1 --> C1_NOTE["Enhances model performance through better input"]
A --> D[Model Development]
D --> D1[Model Selection]
D1 --> D1_EXP{{"Choosing appropriate ML algorithms"}}
D1 --> D1_NOTE["Based on problem type: classification, regression"]
D --> D2[Model Training]
D2 --> D2_EXP{{"Training model on prepared data"}}
D2 --> D2_NOTE["Adjusts model parameters to minimize error"]
D --> D3[Model Evaluation]
D3 --> D3_EXP{{"Assessing model performance"}}
D3 --> D3_NOTE["Uses metrics like accuracy, precision, recall"]
A --> E[Model Implementation]
E --> E1[Model Deployment]
E1 --> E1_EXP{{"Implementing model in production"}}
E1 --> E1_NOTE["Making model accessible for predictions"]
E --> E2[Model Monitoring and Maintenance]
E2 --> E2_EXP{{"Continuous performance tracking"}}
E2 --> E2_NOTE["Ensures model remains accurate over time"]
A --> F[Model Improvement]
F --> F1[Feedback Loop]
F1 --> F1_EXP{{"Refining model based on feedback"}}
F1 --> F1_NOTE["Incorporates new data and insights"]