This course is designed to help you learn Python from scratch, covering various skill levels from beginner to professional.
Data cleaning of a dataset about apartments in Moscow
Welcome to the Data Analysis Fundamentals & Problem Solving repository! This collection of assignments and projects is designed to enhance your skills in data analysis, statistics, and Python programming. The repository covers various aspects of data analysis, from basic statistical measures to complex data-driven modules. l
A placeholder for my work in word embeddings and knowledge graph representations
Regression model to predict foreclosures in Los Angeles using macroeconomic features.
EDA Project on AI GENERATES ART
Binary classification project for predicting client default risk using preprocessing, feature engineering and model comparison.
Rainfall prediction using a tuned Random Forest (GridSearchCV) on weather station data — pressure, humidity, cloud cover, and wind. Built with scikit-learn; includes an honest evaluation of overfitting risk on a small dataset (234 samples).
A data-driven analytics pipeline and machine learning model to evaluate Influencer Marketing ROI and optimize campaign performance.
A simple calculator to find the area of specified range under the normal distribution curve
Plot data availabilty
An end-to-end system for sentiment analysis, topic modeling, and summarization of customer feedback using Python and Hugging Face.
FreeCodeCamp Data Analysis Mini Project
projekt transformacja
A repo made for predicting churned users using kaggle dataset: churn prediction S6E3 it contains the whole process done to get an AUC score of 91.2% on the competition
Complete Python course from basics to AI.
Neste projeto é apresentado um workflow para o desenvolvimento de análises de correlações entre variáveis quantitativas (discretas e continuas) e variáveis qualitativas (ordinal e nominal).
Crime concentrates in hotspots due to offender routines, target availability, and neighborhood characteristics. Analysis uses density mapping, clustering (KDE, DBSCAN), and spatial regression to identify patterns. Time series models (SARIMA, LSTM) predict future crime for resource allocation.
Machine Learning model to predict phone addiction based on behavioral and usage features.
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