involves collecting raw data from various sources and preparing it for analysis. It includes tasks such as data cleaning, where you remove or correct errors, handle missing values, and address inconsistencies.

Data preprocessing techniques like normalization, scaling, and feature engineering are also part of this stage to make the data suitable for analysis.

explore and analyze the prepared data to understand its characteristics, patterns, and relationships. EDA involves using descriptive statistics, data visualization techniques (e.g., histograms, scatter plots, heatmaps), and exploratory techniques (e.g., correlation analysis, clustering) to gain insights into the data. The goal is to uncover trends, anomalies, and key factors that can inform further analysis.

This stage focuses on applying statistical or machine learning models to the analyzed data to derive actionable insights or make predictions. Depending on the objectives, you may use regression analysis for predicting numerical outcomes, classification algorithms for categorical outcomes, clustering algorithms for segmenting data, or other techniques such as time series analysis or natural language processing. After modeling, you interpret the results, evaluate model performance, and derive conclusions or recommendations based on the analysis outcomes. Effective communication of findings through visualization and reporting is crucial in this stage to convey insights to stakeholders and support decision-making processes.