Data Analysis with Python Project

The "Data Analysis Overview" module provides a foundational understanding of data analysis, setting the stage for students to embark on a comprehensive exploration of data analysis techniques and methodologies.

The "Classification Analysis" module delves into various classification algorithms, such as Nearest Neighbors, Decision Trees, SVM, Naive Bayes, and Logistic Regression, empowering students to apply these techniques for predictive modeling tasks.

In the "Regression Analysis" module, students gain proficiency in applying regression algorithms, including Simple Linear, Polynomial Linear, and Linear with regularization, to model and predict numerical outcomes.

The "Clustering Analysis" module explores various clustering techniques, such as partitioning, hierarchical, density-based, and grid-based methods, enabling students to discover underlying patterns and structures in data.

Through the "Dimension Reduction" module, students learn to apply Principal Component Analysis (PCA) to simplify high-dimensional data and aid in data visualization.

The "Association Rules" module equips students with the skills to utilize Apriori and FPGrowth algorithms to mine association rules and discover interesting item associations within transactional data.

In the "Outlier Detection" module, students learn various outlier detection methods, including Zscore, IQR, OneClassSVM, Isolation Forest, DBSCAN, and LOF, to identify anomalous data points and contextual outliers.