Data preprocessing is a crucial step in data mining, as it ensures the data is clean, consistent, and suitable for analysis. This homework focuses on various preprocessing techniques, including data visualization, data augmentation, and data cleaning.

Visualization: Data visualization helps uncover patterns, trends, and outliers in the data. Techniques like scatter plots, histograms, and box plots can effectively represent the data distribution and identify potential issues.

Augmentation: Data augmentation artificially increases the size of the dataset, improving the model's generalization ability. Techniques like random flipping, cropping, and rotating can be applied to create new data instances without altering the underlying distribution.

Cleaning: Data cleaning involves identifying and correcting errors, inconsistencies, and missing values in the data. Techniques like imputation, outlier removal, and data validation can ensure the data is reliable and accurate.

Classification and regression are two fundamental tasks in data mining. This homework focuses on implementing various algorithms for both tasks.

Classification: Classification algorithms predict categorical labels for data points. Techniques like logistic regression, decision trees, and support vector machines (SVM) can be used to classify data based on its features.

Regression: Regression algorithms predict continuous numerical values for data points. Techniques like linear regression, polynomial regression, and neural networks can be used to model the relationship between features and target variables.

K-means clustering is an unsupervised learning algorithm that partitions data into a predefined number of clusters. This homework focuses on implementing the K-means algorithm to group data points based on their similarities.

This project focuses on analyzing data related to Persian music and musicians. The goal is to uncover patterns, trends, and insights into the music genre and its practitioners:

1. Exploratory Data Analysis: Explore the data to understand its characteristics, distributions, and relationships between variables.

2. Feature Engineering: Create new features or transform existing ones to improve the analysis and modeling.

3. Visualizations: Visualize the data to uncover patterns, trends, and relationships.

4. Modeling: Apply appropriate data mining techniques to model the data and extract insights.

5. Interpretation: Interpret the results and communicate the findings effectively.