Course included such topics, as Data Preprocessing, Exploratory Data Analysis (EDA), Statistical Data Analysis (SDA), Data Collection and Storage (PostgreSQL), Business Analytics, Making Business Decisions Based on Data (Hypotheses testing), How to Tell a Story Using Data (Presentation and Data Visualization - Maplotlib, Seaborn, Plotly), Automation (Dash, Tableau), Forecasts and Predictions, 2 Integrated projects.

Project description:You work as an analyst for the telecom operator Megaline. The company offers its clients two prepaid plans, Surf and Ultimate. The commercial department wants to know which of the plans brings in more revenue in order to adjust the advertising budget. You are going to carry out a preliminary analysis of the plans based on a relatively small client selection. You'll have the data on 500 Megaline clients: who the clients are, where they're from, which plan they use, and the number of calls they made and text messages they sent in 2018. Your job is to analyze clients' behavior and determine which prepaid plan brings in more revenue. Description of the plans Note: Megaline rounds seconds up to minutes, and megabytes to gigabytes. For calls, each individual call is rounded up: even if the call lasted just one second, it will be counted as one minute. For web traffic, individual web sessions are not rounded up. Instead, the total for the month is rounded up. If someone uses 1025 megabytes this month, they will be charged for 2 gigabytes.

Research on big telecom company "Megaline". The company offers its clients two prepaid plans, Surf and Ultimate. The commercial department wants to know which of the plans is more profitable in order to adjust the advertising budget. My job was to analyze client's behavior and determine which prepaid plan is more profitable.

Research on the telecom operator Megaline clients' behaviour. The telecom operator Megaline offers its clients two prepaid plans, Surf and Ultimate. The commercial department wants to know which of the plans is more profitable in order to adjust the advertising budget.

This repository contains the complete analysis and machine learning model development for predicting stroke occurrence based on various health and demographic factors. This project was developed as part of the "Statistical Learning and Data Analysis" (SDA) course at the University of Naples Federico II.