This project is a python data analytics project with the aim of valueable information for games or matches played from 1993 to 2022.
The data used for this project comprises of 50 teams of which some have are no longer playing in the top flight while some have been present since 1993 to date.
This project contains five notebooks containing python codes and diagrams showing the finding and again regression analysis to find the best determinants or predictors
to estimate the win percentages for the teams and also cluster analysis to find the relation between certain group of data. Some of the notebooks contains data that have been splitted in to two for better comparison and analysis.
Some have splitted from 1993 to 2000 season and 2001 to 2022 season, some have contains data for home statistics and away statistics as well.
This repository contains notebooks for time series analysis of stock prices using various algorithms such as AR, ARMA, MA, ARIMA, SARMA, SARIMA, auto Arima, and GARCH.
The repository includes projects that use these algorithms to analyze and predict stock prices for FTSE, DAX, NIKKEI, and SPX.
It also includes a project using the Facebook Prophet library to forecast stock prices.
The projects include detailed reports and graphs to show the accuracy of predictions and the impact of different variables.
Overall, the repository demonstrates the effectiveness of time series analysis algorithms and libraries in analyzing and predicting stock prices.
This project involved conducting descriptive analytics on sales data for a multi-national company operating in North America and Europe, specifically France and Germany.
The aim of the analysis was to understand the company's sales patterns and trends in these regions. The analysis included looking at overall sales data as well as data for each region specifically.
Through this analysis, valuable insights were gained into the company's sales patterns and trends, which can be used to inform marketing and sales strategies and make data-driven decisions.
The results of the analysis can help the company improve their performance in these regions.
An analysis of vehicle sales data from 2005 to 2014 was completed, showing that Asia had the highest number of vehicles sold, followed by Europe and America.
Africa had the lowest number of vehicle sales. The data was cleaned using Microsoft Excel before being imported into Tableau for analysis.
The analysis aimed to understand global trends in vehicle sales and identify any patterns or trends in the data.
The results showed that vehicle sales were increasing over time, with the highest number of sales occurring in Asia.
The insights gained from the analysis can be used by car manufacturers and dealers to inform their marketing and sales strategies.
This repository contains projects using Apache Spark to analyze data from various industries, including healthcare, finance, beverages, automobiles, education, real estate, plants, and sports.
The repository includes projects utilizing the Spark ML library and a range of algorithms such as decision trees, factorization machines, gradient-boosted trees, multilayer perceptrons, Naive Bayes, random forests, support vector machines, and various types of regression.
Overall, the repository demonstrates the versatility of Apache Spark and the Spark ML library in analyzing and understanding data from a variety of contexts.
A data analysis project using Tableau was completed for a large bank in the UK. The project aimed to identify trends and insights related to the bank's customers, including their age, account balance, region, and occupation.
The analysis revealed that the majority of the bank's customers were between the ages of 15 and 45, had account balances under $10,000, were located in urban areas, and had a diverse range of occupations.
The results of the project provided the bank with valuable information to help them target their marketing efforts more effectively.
This repository contains machine learning projects using a variety of algorithms to analyze data from different industries including business, healthcare, sports, marketing, and plants.
The algorithms used include linear regression, logistics regression, Naive Bayes, support vector machines, decision trees, random forests, k-nearest neighbors, k-means, and neural networks with TensorFlow.
One project uses logistics regression and SVC to predict outcomes in healthcare and sports data, with detailed reports and graphs showing accuracy and the impact of variables.
Overall, the repository showcases the capabilities of machine learning algorithms in different contexts.
A Covid-19 data analysis was completed using Tableau with data on cases, deaths, and recoveries for various countries.
The analysis aimed to understand the impact of the pandemic and identify any trends or patterns in the data.
The results showed that the number of cases was increasing in most countries and that the pandemic was affecting different regions differently.
The analysis also revealed that the number of deaths and recoveries varied significantly between countries.
The insights gained from the analysis can be used to inform public health policies and strategies to address the pandemic.
A descriptive analysis of salary data for different industries and counties in the USA was completed using Tableau.
The analysis aimed to understand salary patterns and trends for different industries and locations in the country.
The results showed that certain industries had higher salaries than others and that salary levels varied significantly between different areas of the country.
The analysis also revealed that certain industries tended to have higher salaries than others.
The insights gained from the analysis can be used to inform career and job search decisions for individuals in different industries.