Data Engineering project using Databricks PySpark & Spark SQL for analysing data from Spotify API and present in form of PowerBI report

Repository of my various data analysis portfolio projects done in Python, SQL and PowerBI.

A python based project to predict the future prices of the top 10 trending cryptocurrencies using ML Algorithms like SVR, Decision Tree and LSTM with an interactive frontend using streamlit. Analysis using PowerBi and has DBMS connectivity.

Empower your business with "PowerBI Customer Segmentation Analysis" – a dynamic Power BI project for in-depth customer segmentation. Unleash valuable insights, visualize trends, and optimize strategies with this robust repository, leveraging the full potential of your customer data.

This PowerBI project focuses on visualising data for Apocalypse Food Prep, a company specialising in emergency food supplies. The dataset consists of various CSV files containing information on customers, locations, products, sales, sales teams, and state regions.

This project, leveraging the power of Power BI, dynamic dashboard for intuitive decision-making.🌐 Visualize sales metrics with dynamic charts 📈 Identify customer trends, predict future sales, and navigate your sales journey seamlessly. 🚀 Empower your team with actionable insights! #DataDrivenSuccess #PowerBI #SalesAnalytics 🚀📈🔍

This project demonstrates the practical experience of designing database systems, data modelling using ER Diagram, normalization of historic data, ETL Technologies, Designing multidimensional CUBE structures and Dashboard reports using PowerBI.

This project extracts data having 800k records from CSV in the data factory and convert it to parquet based data and finally create a PowerBI report of that parquet based data.

"30DaysDataAnalytics: A month-long adventure using SQL, Python, and Power BI to explore a variety of datasets. Come along as we develop 30 data analytics projects and hone your data analysis skills. 🚀📊 #DataAnalytics #SQL #Python #PowerBI"

ANALYZING ROAD SAFETY & TRAFFIC DEMOGRAPHICS IN THE UK (Multi-class Classification) SUMMARY Here, I am aim to analyze the Road Safety and Traffic Demographics dataset (UK), containing accidents reported by the police between the years of 2004 - 2017. PROJECT GOALS: Identify factors responsible for most of the reported accidents. Build a machine learning model that is capable of accurately predicting the severity of an accident. Provide recommendations to the Department of Transport (UK Government), to improve road safety policies and prevent recurrences of severe accidents where possible. PACKAGES USED: Scikit-learn, numpy, pandas, imblearn (imbalanced-learn), seaborn, Matplotlib MOTIVATION World Health Organization (WHO) reported that more than 1.25 million people die each year while 50 million are injured as a result of road accidents worldwide. Road accidents are the 10th leading cause of death globally. On current trends, road traffic accidents are to become the 7th leading cause of death by 2030 making it a major public health concern. Between the years 2005 and 2016, there were roughly 2 million road accidents reported in the United Kingdom (UK) alone of which 16,000 were fatal. As a big data project, I wanted to explore the traffic demographics data in greater detail using machine learning! CONTEXT The UK government amassed traffic data from 2004 to 2017, recording over 2 million accidents in the process and making this one of the most comprehensive traffic data sets out there. It's a huge picture of a country undergoing change. Note that all the contained accident data comes from police reports, so this data does not include minor incidents. For steps undertaken to pre-process and clean the data, please view the "Data Cleansing & Descriptive Analysis_UK Traffic Demographics.ipynb" file DESCRIPTIVE ANALYTICS (EDA) Tools used include Python, Tableau, MS PowerBI Percent (%) distribution of target classes Percent dist of Accident Severity As seen above, the data is highly imbalanced. For detailed steps undertaken to deal with the imbalanced data, please view the "Modelling_Predictive Analytics_UK Traffic Demographics.ipynb" file. This article provides some great tips on utilizing the correct performance metrics when analyzing a models performance trained on an imbalanced dataset. This article describes several strategies that can help combat the case of a severly imbalanced dataset. Methods include: Resampling strategies (under - Tomek Links, Cluster Centroids, over sampling - SMOTE) Using Decision Tree based models Using Cost-Sensitive training (Penalize algorithms) Number of accidents by Year and Accident Severity Total accidents by year and severity It can be seen above that the trend seems to be increasing as the years go. In addition, the spike between 2008 - 2009 was because of a enhancement in the reporting system introduced in the UK in 2009, where all accident including minor accidents needed to be reported by the police so as to match the counts represented by hospitals, insurance claims etc. Accidents density by Location geomap Most accidents took place in major cities - Birmingham, London, leeds, Newcastle Accidents by Gender and Age Accidents by gender and age Accidents by Day of the week and Year Accidents by year and weekday Most accidents take place on a Friday Vehicle Manoever at time of accident Vehicle Manoever at time of accident Most accidents take place as a result of overtaking For more findings, please go to the "Images" folder. For steps undertaken to carry out some predictive modeling and hyper-parameter tuning, please view the "Modelling_Predictive Analytics_UK Traffic Demographics.ipynb" file. RECOMMENDATIONS TO THE DEPARTMENT OF TRANSPORT (UK) Decrease emergency response times during afternoon rush-hours (15-19) especially on Fridays. Allocate resources to investigate high density traffic points and identify new infrastructure needs to divert traffic from dual-carriage ways. Explore conditions of vehicles and casualties such as vehicle type, age of vehicles registered, pedestrian movements, etc. for policy makers. Adopt comprehensive distracted driving laws that increase penalties for drivers who commit traffic violations like aggressive overtaking. ACKNOWLEDGEMENTS The license for this dataset is the Open Givernment Licence used by all data on data.gov.uk. The raw datasets are available from the UK Department of Transport website. I had a lot of fun working on this dataset and learned a lot in the process. I plan to further my research in the area of predictive modeling using imabalanced data and how to effectively build a highly robust model for future projects. About Here, I analyze the Road Safety and Traffic Demographics dataset (UK), containing accidents reported by the police between the years of 2004 - 2017. Topics accident-rate accident-severity imbalanced-data imbalanced-learning road-accident reported-accidents road-safety uk-government transport traffic-demographics severe-accidents pca classification Resources Readme Releases No releases published Packages No packages published Languages Jupyter Notebook 100.0% © 2020 GitHub, Inc.

A multipage PowerBi Project for Laptop Sales of India 2024.

A collection of Data Analysis Projects in Excel, SQL, Python & PowerBI

A Collection of All Projects Done in PowerBI, Excel, SQL Python

Data Analysis Project of Customer Behavior Analysis Using Python , SQL And PowerBI

This project is about a PowerBI dashboard project created to track sales performance of existing dataset

This is a basic PowerBI project which as a small amount of dataset related sales of a small store.

It Was a Certification Task Provided By TATA Group of Companies. The Data Visualization Project Was Completed Using POWERBI

This repository contains a collection of Data Analytics projects and scripts aimed at performing ETLs, SQL, Dashboards in PowerBI

Data Analyst Portfolio Project 2 - PowerBI + Power Query - A survey of data professionals across industries. Showcasing my Power Query data transformation skills.

In this project, we prepared a PowerBI dashboard using the data of an e-commerce company that does Amazon Dropshipping in the USA. This interactive PowerBI will be very beneficial for anyone who wants to improve themselves in this field.

Create sales insight data analysis project using SQL and PowerBi to gain a deeper insight about the sales and the functioning of differnt markets.

Explored the dynamic world of pizza sales through a detailed Power BI analysis.Excited to apply these insights to future projects! #PowerBI #DataAnalysis #PizzaSales 🚀

This Repository is based on Hospitality Project.This repository has a set of projects and exercises that showcases my skills as a Data Professional, who is comfy in data analysis and science using SQL, EXCEL, and, POWERBI

This project challenged us to process and integrate data coming in from a variety of sources - IMDb data, movie rating data from MovieLens, and The Numbers data. We utilized multiple features of the Talend and SQL server to optimize the complete ETL process We then visualized the integrated data from our dimensional model in #Tableau and #PowerBI dashboards showcasing some core KPIs and metrics. This project helped us understand the various challenges faced while building data integration pipelines and business intelligence applications.

This repository contains the PowerBi and Excel Based projects on the PK Covid data set. In which the data was cleaned and Data visualization is focused to know the impact of COVID on Pakistan.

This project uses PowerBI Desktop to provide broad audiences with a comprehensive analysis of lodging properties listed on Airbnb in Bangkok, Tokyo, Taipei, Hongkong and Singapore to deliver meaningful insights into listings in these markets.

Empower your business with "PowerBI Customer Segmentation Analysis" – a dynamic Power BI project for in-depth customer segmentation. Unleash valuable insights, visualize trends, and optimize strategies with this robust repository, leveraging the full potential of your customer data.

The purpose of this project is to monitor vital signs such as heart-rate and temperature. The system automatically sends alerts to healthcare if there is any abnormality in the telemetry value. The IoT device is simulated using the Python code, which generates the Heartrate and Temperature. The values are sent to the Azure IoT Hub. Using Stream Analytics Job the data from the IoT Hub is sent to the PowerBI to generate meaningful insights (Visualization). The notification is sent using Azure Logic Apps by connecting IoT hub and mailbox (SendGrid).

This project is currently at realization and mainly consists of : *Extract - Transform - Load : Apply ETL process on Clients financial databases (based on Sage 100 ERP) to centralize it into a local data warehouse using Talend . *Data warehousing : Establish the architecture and relations of different components of the data warehouse making it ready for OLAP proceeding . *Data Analysis & Visualization : Perform sectoral analysis and establish real-time graphs according to the related sector metrics using PowerBI . *Prediction : Perform basic machine learning models having medium accuracy indicators to predict simple sectoral metrics using R studio .

As we all know Climate change has impacted many parts of the world and disturbed the Sustainability of Mother Earth so we ( Alison Rebello, Abrahdeep Chakraborty & me ) thought to make a project on Climate change using Power BI and MS excel to show that it is impacting us in a big way. It is also impacting 17 Sustainable development goals of the United Nations and their quest toward Sustainability. We the students of Goa Institute of Management (GIM) are very concerned about Sustainability and this is a small step from us in form of the Climate Action Dashboard using Power BI in the direction of Sustainability. Major Impacts include Global warming, Climate refugees, Soil Degradation, a threat to Food Security, Political Stability, etc. In the Visualisation: 1) We have made a visualization where there is an animated bar chart showing the highest temperature of countries from 1995 to 2020. 2) Lowest temperature countries are represented with the help of a pie chart. 3) Animated Heat Map which depicts variability of temperature across the world from 1995-2020. The dataset is taken from Kaggle. #powerbi #data #sustainability #climatechange #food #security #sdgs #unitednations #sustainabledevelopment #visualizations #climateaction #dashboard #globalwarming #kaggle #project #bigdata #dataanalysis #gim #management