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-colored-squares

a-b-testing-t-test-example-fare-amount-for-taxi

This project is made on the basis of the learning task in Google certification "Advanced Data analytics". Project Goal: explore the relationship between the type of payment and the amount of fare a customer pays. A/B testing implementation: scipy.stats.ttest_ind().

aim-game

automatidata-case

This project is based on the Google Advanced Data analytics certification training assignment. The goal of this model is to predict whether or not a customer is a generous tipper. Implementation: sklearn.ensemble.RandomForestClassifier() and xgboost.XGBClassifier().

binary-logistic-regression-model-example

This project is based on the Google Advanced Data analytics certification learning assignment. The goal of the project: to predict whether a customer will be satisfied with a company's services. Since the outcome variable is a binary variable, a Binary Logistic Regression Model was built. Implementation: sklearn.linear_model.LogisticRegression().

css-cv

data-analysis-implementation-of-some-steps

This repository contains files that implement some aspects of data analysis at various stages of dataset manipulation.

decision-tree-model-example

This project is based on the Google Advanced Data analytics certification training assignment. The goal of the project: to develop a model that can predict whether a customer will be satisfied ( the outcome variable is binary: 0 - dissatisfied, 1 - satisfied) with the services of an airline. Implementation: sklearn.tree.DecisionTreeClassifier().

drag_n_drop

git_demo

hypothesis-testing

This repository contains files with some hypothesis testing steps. The repository is gradually being updated with new files. These variants of data hypothesis testing were performed as part of Google Advanced Data Analytics certification training assignments.

k-means-algorithm-example

This project is based on the Google Advanced Data analytics certification training assignment. The aim of the project: to partition a dataset with information about penguins and check how many species of penguins are collected in the dataset and whether there are hidden relationships. Implementation: sklearn.cluster.KMeans().

multiple-linear-regression-model-example

This project is based on the Google Advanced Data analytics certification training assignment. The goal of the project is to develop a model that can predict the cost of a trip for a taxi customer. Multiple Linear Regression was chosen as the model. Implementation: sklearn.linear_model.LinearRegression().

multiple-linear-regression-model-ols-example

This project is based on the Google Advanced Data analytics certification training assignment. The goal of the project is to develop a model that can predict sales depending on where the advertisement is placed (radio, TV, social media). Multiple Linear Regression was chosen as the model. Implementation: statsmodels.formula.api.ols().

naive-bayes-model-example

This project is based on the Google Advanced Data analytics certification training assignment. Gaussian Naive Bayes has been chosen as the model. Implementation: sklearn.naive_bayes.GaussianNB().

preparing-dataset-for-analysis-and-model-building

This repository contains files with some steps to prepare the database for its further analysis and model building. The repository is gradually being updated with new files.

random-forest-model-with-tunning-parameters

This project is based on the Google Advanced Data analytics certification training assignment. The goal of the project: to develop a model that can predict whether a customer will be satisfied ( the outcome variable is binary: 0 - dissatisfied, 1 - satisfied) with the services of an airline. Implementation: sklearn.ensemble.RandomForestClassifier()

rock-paper-scissors

You can play in this game with your computer!

salifort-motors-student-case

This project was carried out on the basis of the final Google Advanced Data Analytics certification project. The results of the project include research and preparation of information for building suitable models, construction of necessary visualisations, training and testing of models, as well as executive summary.

simple-linear-regression-model-examples

This project is based on the Google Advanced Data analytics certification training assignment. The goal of the project is to predict sales based on radio and TV advertising data separately. Simple Linear Regression was chosen as the model. Implementation: statsmodels.formula.api.ols().

slider

slider-2.0

storage_and_truck

New shipping project

web-site

webb-ssite

xgboost-model-example

This project is based on the Google Advanced Data analytics certification training assignment. Project goal: to develop a model that can predict whether a customer will be satisfied (the outcome variable is binary: 0 - dissatisfied, 1 - satisfied) with an airline's services. XGBoost was chosen as the model. Implementation: xgboost.XGBClassifier().