Projects completed as part of the "Data Science Specialist" course at Yandex.Practicum.
| Navigation | Project Name | Description | Main Tools |
|---|---|---|---|
| final_customer_churn | Final Project - Customer Churn Prediction for a Telecom Operator | The telecom operator "NoDisconnect.com" wants to predict customer churn. | pandas, numpy, scipy, sklearn, catboost, lightgbm, matplotlib, seaborn |
| age_clients | Customer Age Recognition for a Store | Computer vision project to determine the age of store customers based on photos. | keras, sklearn, pillow, pandas, numpy, matplotlib, seaborn |
| toxic_comments | Detecting Toxic Comments | Machine learning for text. Building a model to identify toxic comments for moderation in an online store. | nltk, spacy, sklearn, wordcloud, lightgbm, matplotlib, seaborn |
| taxi_order_prediction | Taxi Order Forecasting | Time series analysis. Developing a model to predict the number of taxi orders for the next hour based on historical airport order data. | statsmodels, sklearn, lightgbm, matplotlib, seaborn, pandas, numpy |
| car_cost_prediction | Determining Car Prices | Numerical methods. Determining the market value of cars for a car sales service. | sklearn, lightgbm, catboost, matplotlib, seaborn, pandas, numpy |
| protection | Customer Personal Data Protection | Linear algebra project to develop a data transformation method for customer data. | sklearn, lightgbm, catboost, matplotlib, seaborn, pandas, numpy |
| gold_recovery | Gold Recovery from Ore | Composite project - 2. Developing a machine learning model prototype to optimize gold recovery from ore. | sklearn, scipy, matplotlib, seaborn, pandas, numpy |
| customer_churn_bank | Bank Customer Churn | Supervised learning. Predicting customer churn based on historical customer behavior and contract terminations with the bank. | sklearn, matplotlib, seaborn, pandas, numpy |
| bank_clients | Bank Client Creditworthiness Analysis | Data preprocessing. Studying the impact of customer parameters on loan repayment. | pymystem3, pandas, numpy |
| best_tariff_predict | Telecom Tariff Recommendation | Introduction to machine learning. Building a model to recommend the most suitable mobile operator tariff based on customer behavior data. | sklearn, matplotlib, seaborn, pandas, numpy |
| best_tariff | Telecom Operator Tariff Analysis | Statistical data analysis. Analyzing customer behavior based on user data to determine the most promising company tariff. | pandas, numpy, matplotlib, seaborn |
| game_sales | Video Game Sales Analysis | Composite project - 1. Investigating potentially popular products among video games and consoles. Finding patterns based on historical data and ratings. | pandas, numpy, matplotlib, seaborn |
| real_estate | Real Estate Listings Analysis | Data exploration project. Analyzing the real estate market in St. Petersburg and neighboring areas. Identifying factors influencing property market prices. | pandas, numpy, matplotlib, seaborn |
| well_location | Best Well Location | Data exploration project. Analyzing well location. | pandas, numpy, matplotlib, seaborn |