Extract and select features (or construct new ones) to prepare your dataset for machine learning with genetic algorithms, random search, or backward- and forward feature elimination. Manipulate text, apply formulas on numerical data, and apply rules to filter out or mark samples.Build machine ...
embark on a fascinating exploration of the legendary Titanic dataset and uncover its secrets through data cleaning, visualization, and feature engineering. Over the course of the course, you will delve into the intricacies of machine learning, use logistic regression to build predictive models, and e...
Lecture 36 Ridge Regression-Assignment Section 10: Lasso Regression Lecture 37 What is Lasso regression and practice program-1 Section 11: ElasticNet Regression Lecture 38 what is ElasticNet Regression and practice program-1 Section 12: Logistic Regression Lecture 39 What is Logistic Regression and pr...
2022.123.03.2022Agglomerative Hierarchical Clustering: New truncation methods, extra graphs, the ability to color dendrograms according to groups. Statistical Process Control: New functionalities, more ergonomic UI, the ability to select variables from a drop-down list or to call a dataset from a txt...
Data Exploration: Explore the dataset using descriptive statistics and visualizations to understand its characteristics. Statistical Analysis: Perform the desired statistical analyses such as t-tests, ANOVA, or chi-square tests based on your research questions. Interpret Results: Interpret the results of ...
Logistic regression by hands: [](https://colab.research.google.com/github/ml-mipt/ml-mipt/blob/basic/week03_logistic/week03_logistic_regression.ipynb) Logistic regression for image classification: [_LogisticRegression.zip 2024-12-26 06:34:54 ...
Lecture 13 How to Use ChatGPT to Generate a Simulated Dataset for Factor Analsyis Lecture 14 Using ChatGPT for Manual Calculation of Factor Loadings Lecture 15 How to Use ChatGPT to Generate APA Style Tables Lecture 16 Using ChatGPT to generate APA style Write Up ...