OBJECTIVE Ecommerce or Retail Store wants us to help them create a dashboard to track and analyze their online sales across India to improve customer buy rate and sales.

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 project leverages Power BI for data visualization, DAX for custom calculations, and integrates SQL and Excel for data preprocessing, analysis, and reporting, enabling dynamic and interactive insights.

This Repository contains all my Data Analysis Dashboards Projects that I have created using PowerBi. Excel, SQl & Python is also used for data cleaning process.

In this project I used the survey data from Alex Freberg .. I cleaned the data using python pandas & numpy before exporting the data back as an Excel file to be used for final analysis and visualization on 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.

Data analytics project for cutomer_analysis using python,.sql and powerBI

The folder contains the data analysis and visualization project using python for analysis and the summary is represented through PowerBi

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 project begins with loading the csv files from the **data folder** and converting them into a single Excel file for easy visualisation and analysis on Microsot PowerBI(*Tableau can also be used*); kindly check eda.ipyb file

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 .

Various projects for Data Analysis using Excel, Python, MySQL, PowerBI, etc.

First PowerBi project for data analysis, completed as part of Alex the Analyst's bootcamp.

Find the attached samples from my Self Project - Sales Insights Data Analytics - Data Analysis in PowerBI - Visualization of the Data Analyisis, findings, and insights. For the Powerbi Interactive portfolio - visit my NOVYPRO Profile.

Find the attached ssamples of my Self Project done for Swiggy Restaurant data analysis in PowerBI - Visualization of the Data Analyisis, findings, and insights. For the Powerbi Interactive portfolio - visit my NOVYPRO Profile.

Zomato Data Analysis - Explore the world of Zomato restaurant data through Python and data analysis. Uncover trends and insights using Pandas for data manipulation and Matplotlib for visualization. Project has dashboard by using a PowerBI tool

Data Analysis Project : Interactive Power BI Dashboard for a company providing valuable insights based on 4 years data. Tech Stack : PowerBI, ETL, SQL, MySql Workbench

Find the attached samples from my Self Project - 120 Years history of Olympics Dataset -Sports Data analysis in PowerBI - Visualization of the Data Analyisis, findings, and insights. For the Powerbi Interactive portfolio - visit my NOVYPRO Profile.

Business Insights 360: A PowerBi Project for AltiQ Hardware. This repository features a PowerBi dashboard enabling data-driven decisions in sales, finance, marketing, and supply chain. It highlights data modeling, visualizations, and reports for strategic business analysis and planning.

This repository contains the documentation for the Genernation Hong Kong x Clap@JC JDE Final Project, which focuses on data analysis and PowerBI tools for market insights.

Discover dynamic data analysis projects powered by PowerBI. Uncover insights, trends, and patterns in diverse datasets. From sales analytics to customer behavior, explore actionable insights for informed decision-making.

This repository showcases my HR Employee Distribution Analysis project, leveraging SQL for data extraction and transformation, along with PowerBI for insightful visualizations. Explore the data-driven insights that inform HR decision-making

Conduct basic data analytics and visualization on the Weather Data Set to gain insights. No machine learning is required for this entry-level project, and PowerBI tool can be used for analysis and visualization.

An end-to-end data analysis project using SQL and PowerBI to create a dashboard for a fictional company "Toman Bike Share" to display their metrics and KPIs for informed decision-making

This is a interactive dashboard for Churn analysis using MySQL (SSMS). PowerBI, Python. The Comprehensive Churn Prediction and Analysis project leverages data analytics and machine learning techniques to predict customer churn and analyze key factors contributing to it.

This project offers a detailed analysis of the 2024 Indian General Election, focusing on major political party performance, voter turnout statistics, and unique election data. It includes a PowerBI dashboard for visualizing results, enhancing understanding. This analysis is part of the Kalvium Data Analyst recruitment process.

This project is part of the series of transition from MS Excel to R's Tidyverse package for a better pivoting and Data Mining for further analysis. End users can pull the data into Excel and further to the dashboards using PowerBI.

xploratory Data Analysis (EDA) project on a Portuguese bank marketing dataset. Leveraged mysql, powerBI, Python libraries such as Pandas, Matplotlib, and Seaborn to analyze customer behavior and identify key features influencing subscription to term deposits. This analysis helps derive business insights for targeted marketing strategies.

This project explores credit risk assessment using real-world credit card default data. The goal is to build a predictive model to determine the likelihood of default, using MySQL for data storage, Python for Analysis and PowerBI for visualisation. The project follows best practices to demonstrate an end-to-end financial DS workflow.

This project is a Power BI dashboard that provides insights into weather conditions and air quality. It includes visualizations for temperature trends, humidity, wind speed, and pollution levels (PM2.5, CO, NO2, etc.), helping users understand environmental patterns through interactive charts.powerbi dashboard data-visualization data-analysis