Each folder had a different project in it, where in the readme file you can:
- Open a link to the notebook and see the project.
- Open a link to a few favorite graphs and explanations about them (a subfolder of the project folder).
- Keep on reading the readme file and get explanation and summary of the project and it's conclusions.
The following table has my projects sorted in my preferred order from top to bottom:
| Project | Description | Libraries | Featured stunts |
|---|---|---|---|
| Tel Aviv accidents analysis - Anyway | This is the first project done for Anyway, using data from Halamas on accidents in tel aviv | pandas, numpy | categorization, creating tables for further analysis |
| Product Range Analysis - International Online Shop | A freestyle product range analysis of a large E-Commerce service, Answering relevant questions and finding highe revenue products and categories. | pandas, numpy, matplotlib, nltk, scipy.stats, seaborn, mlxtend - association_rules, apriori | huge regex based categorization, market basket analysis using apriori, hypothesis testing, data preprocessing |
| A/B testing and analysis- Online shop | Examining, fixing, and continuing the work of a previous analyst fella who has launched an A/B test for a new recommendation system to an international online store. Then one clear day - in the middle of everything, he decided to quit and start a watermelon farm in brazil:bangbang: They left only the technical specifications and the test results. | pandas, numpy, matplotlib, seaborn, plotly, math, scipy | data preprocessing, user behavior analysis, A/B testing, multiple tests statistical correction |
| Forecasts and predictions project - Model Fitness Gym | in this customer behavior analysis ML is used to predict churn rate for the gym's customers. both linear regression and random forest are employed to determine which is the best algorithm to do the job. | pandas, numpy, seaborn, matplotlib, scipy.cluster.hierarchy, sklearn | ML forecasting - random forest / linear regression, k-means clustering, feature correlation matrix heatmap (yes all in one!) |
| A/B Analysis - Hypothesis testing and prioritization | A marketing team of a large online store has compiled a list of hypotheses that may help boost revenue. In this project we prioritize the hypotheses and execute the most urgent one. | pandas, numpy, seaborn, matplotlib, scipy | prioritization comparing ICE / RICE, multiple A/B Testing and statistical correction |
| Market analysis - Cafe Botz) | With the intention to open a small robot-run cafe in Los Angeles and attracting investors who are interested. It is necessary to conduct a research of the restaurant market of LA. | pandas, matplotlib, seaborn | market research, Data preprocessing, regex power to clean and categorize streets and venues |
| Business Analysis - Yandex.Afisha | Yandex.Afisha — the two teamed up in 2014 to present Russians with the option to search showtimes and pre-purchase tickets online. from home or on android and ios supported phones. The goal is to help optimize marketing expenses of different ad source campaigns, and of course analysing user behaviour. | pandas, numpy, seaborn, matplotlib, scipy | cohort analysis, business metrics, data preprocessing, user behavior analysis |
| User Behaviour analysis and A/A/B testing | This project is about running and analyzing the results of an A/A/B testing for a new font in the app at a startup company in the food product market by investigating user behavior for the company's app, and decide wether the experiment was successful. | pandas, matplotlib, seaborn, plotly, scipy/stats, math | event funnel analysis, A/B testing, multiple tests statistical correction, event funnel analysis, data preprocessing |