Work Samples

• Correlation and Covariance: Analysis of correlation and covariance between variables.
• Data Cleaning: Techniques for cleaning and preprocessing data.
• Data Input and Output: Methods for reading and writing data.
• DataFrames with Pandas: Working with DataFrames in pandas.
• Dictionary Operations: Operations and manipulations with dictionaries.
• Functions: Creating and using functions in Python.
• KMeans Customization: Customizing KMeans clustering.
• KMeans Practice: Practical examples of KMeans clustering.
• KMeans: Implementation of KMeans clustering.
• Lambda Functions: Using lambda functions in Python.
• Lambda Function Examples: Examples of lambda functions.
• Linear Regression: Implementing linear regression.
• List Demonstrations: Demonstrations of list operations.
• Logistic Regression: Implementing logistic regression.
• Advanced Matplotlib: Advanced plotting with Matplotlib.
• Matplotlib Graphs: Creating graphs with Matplotlib.
• MaxMinScaler and Encoding: Scaling and encoding data.
• Methods: Defining and using methods in Python.
• MinMaxScaler: Using MinMaxScaler for data normalization.
• Multivariate Regression: Implementing multivariate regression.
• Multivariate Regression Hypothesis: Hypothesis testing in multivariate regression.
• Numpy Arrays: Working with arrays in Numpy.
• Numpy Practice 2: Practice exercises with Numpy.
• Numpy Practice 1: Basic practice with Numpy.
• Polynomial Regression: Implementing polynomial regression.
• Project 1: First project analysis.
• Project 2: Second project analysis.
• Sample Notebook: Sample Jupyter notebook.
• Standard Deviation and Variance: Calculating standard deviation and variance.
• SVM: Implementing Support Vector Machines.