Exploratory Data Analysis and solving a classification problem

Overview of Exploratory Data Analysis (EDA): Understand the role of EDA in discovering patterns, relationships, and anomalies within datasets.
Data visualization techniques: Learn to use charts, graphs, and plots (like histograms, scatter plots, and box plots) to make sense of data.
Handling missing and outlier data: Explore strategies for dealing with incomplete or extreme data points.
Data preprocessing: From scaling and normalization to handling categorical variables, learn how to prepare data for machine learning.
Feature engineering & selection: Identify key features that influence model performance.
Classification problem-solving: Build a machine learning classification model using real-world data.
Model training and evaluation: Use techniques like cross-validation, confusion matrix, and accuracy metrics to evaluate model performance.
Improving model performance: Implement strategies such as hyperparameter tuning to enhance the model's accuracy.