Project Details

Objective:
Explore the dataset and extract actionable insights that will enable growth in the market.
Perform univariate and multivariate analysis.
Building customer profiles (characteristics of a customer) for the different products.
Generate set recommendations that will help the company in targeting new customers.

Data:
CardioGoodFitness.csv - it contains information about customers purchasing products.
Product - the model no. of the treadmill
Age - in no of years, of the customer
Gender - of the customer
Education - in no. of years, of the customer
Marital Status - of the customer
Usage - Avg. # times the customer wants to use the treadmill every week
Fitness - Self-rated fitness score of the customer (5 - very fit, 1 - very unfit)
Income - of the customer
Miles- expected to run

Project Details

Project Description:

In this project, the goal is to create one report which could be used by stakeholders from sales, marketing, finance and executive teams. The focus is made on the following

1. Robust Data Modeling
2. User-empathetic Report design
3. Drillable Insights

I learned the following things in this project

1. Power Query (Basic and Advanced Operations)
2. Basic and complex DAX formulas
3. Data modelling involving 10+ tables
4. Choosing the right visuals and formatting
5. Dashboard designing principles
6. Using bookmarks
7. Deploying in Power BI service
8. Stakeholder Feedback Implementation
9. Sales, Marketing, Finance & Supply Chain metrics

Live Interactive Dashboard

Project Details

In this project, I created a sales report using excel pivot tables which will enable businesses to determine effective customer discounts, facilitate negotiations with consumers, and identify potential business expansion opportunities in promising countries. The reports generated will provide actionable insights and recommendations for businesses to optimize their sales strategies, enhance customer satisfaction, and maximize revenue generation.

I have learned the following:

1. Pivot table
2. Power Query
3. DAX (Basics)
4. Conditional Formatting
5. Report Beautification
6. Sales Domain Metrics

Overall, this project aims to empower businesses with the necessary tools and insights to monitor, evaluate, and enhance their sales activities. By leveraging sales data analysis, businesses can unlock new opportunities, drive growth, and stay competitive in the dynamic market landscape.

Project Details

Leveraging Tableau, my project analyzes COVID-19 data from Our World in Data up to June 8, 2021. The interactive dashboard showcases the pandemic's toll through various graphs and maps, highlighting total deaths by continent, a comparative analysis of case and mortality rates, and vaccination progress in Nepal. It provides an intuitive platform for exploring data on global and regional scales, illustrating the disparities in health outcomes and vaccination coverage worldwide. This visualization serves as a resource for understanding pandemic trends and informs strategies for public health interventions. Access the full suite of visualizations and uncover insights into the COVID-19 landscape at Explore the visualization at https://public.tableau.com/app/profile/niraj.rokaya/viz/CovidEffectsUntilJune82021/Dashboard1

Project Details

Overview:
In this project, the objective is to predict the selling price of residential properties, focusing on key factors such as transaction_date, house_age, transit_distance, local_convenience_stores, latitude, and longitude. The target variable is 'price_per_unit,' measured in units of 3.3 square meters. The dataset is sourced from a study by Yeh and Hsu (2018) and is available on the UCI ML Repository.

Challenge Objectives:
Conduct thorough data exploration and preparation.
Identify predictive features for estimating 'price_per_unit.'
Develop a regression model aiming for the highest accuracy.

Methodology:
Utilize regression models: Linear Regression, Decision Tree, Random Forest, Gradient Boosting, and XGBoost.
Initial preference for Random Forest due to its higher accuracy (72%).
Develop and integrate a preprocessing pipeline, resulting in an accuracy of 71%.

Results:
The refined model consistently predicts real estate prices with approximately 71% accuracy.

Project Details

The Road Accident Dashboard project aims to provide comprehensive analytics on traffic incidents for 2021 and 2022. It will feature key performance indicators (KPIs) including total casualties and accident counts, year-over-year growth, and detailed breakdowns by accident severity, vehicle type, and road conditions. The dashboard will offer monthly trend analyses and differentiate between day and night incidents across various locations, enabling stakeholders to derive insights and enhance safety measures.

Live Interactive Dashboard