Steps underwent in treating the data
This data science project follows an eight-step process:
- Problem Definition : Clearly articulate the challenge or business question to address with data-driven insights.
- Domain Understanding : Acquire in-depth knowledge of the business area or industry context, focusing on key stakeholders and processes.
- Data Acquisition : Extract the relevant dataset from available sources, ensuring it meets the project requirements.
- Data Cleaning and Preprocessing : Perform data transformation, handling missing values, and correcting inconsistencies to prepare for analysis.
- Exploratory Data Analysis (EDA) : Conduct comprehensive EDA to understand data distributions, identify patterns, and detect anomalies.
- Model Development : Construct and train machine learning models using appropriate algorithms and techniques to meet the project objectives.
- Result Interpretation : Analyze the model outputs, validate the results, and extract actionable insights.
- Deployment and Production : Implement the final model in a production environment, ensuring scalability, reliability, and monitoring capabilities.