Collection of all SQL projects and training I have completed.
This repository contains code for the following projects:
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Bellabeats
- Analysis on Fitbit data for 33 different users.
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Anime Ratings
- Analysis of over 20,000 animes to determine what makes an anime successful. Power Bi visualization was created for this analysis:
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SuperStore Dashboard
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Power Bi Report - Analysis of the SuperStore dataset made a dashboard in the this analysis i find out the Profit by Category, Technology, Segment, Region, Year, and quarter
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COVID Worldwide:
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Analysis on cases, testing, recovery, and deaths in 231 countries. Analysis on over 1 million anonymous patient records from the Mexican Government.
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Power Bi Report - created a dashboard in Powerbi summarizing the data.
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I analyzed a dataset using R for a company, Cyclistic, a bike sharing company in Chicago. Created an interactive dashboard showing how annual members and casual riders use Cyclistic bikes differently. I also provided marketing strategies to convert casual riders to annual members.
Cyclistic is a fictional bike sharing program which features more than 5,800 bikes and 600 docking stations. It offers reclining bikes, hand tricycles, and cargo bikes, making it more inclusive to people with disabilities and riders who can't use a standard two-wheeled bike. It was founded in 2016 and has grown tremendously into a fleet of bicycles that are geotracked and locked into a network of 692 stations across Chicago. The bikes can be unlocked from one station and returned to any other station in the system anytime.
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Power Bi Report - created a Report in Power bi summarizing the data.
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- CyclistBikeShare.R - analyzed the data set from case study 1 in the Google Data Analytics Course using R. I did not do any data visualization in R.
- CyclistBikeSharePowerbi.R - create a specific data frame to use in Powerbi. Deleted unncessary columns to make the code run quicker in Powerbi. Power Bi visualization was created for this analysis:
- Diwali_Sales_Analysis.R
- Performed data cleaning and manipulation
- Performed exploratery data analysis(EDA) using ggplot2, dplyr packeges
- Improved customer experience by identifying potential customers across different states, occupation, gender and age groups
- Improved sales by identifying most selling products categories and products, which can help to plan inventory and hence meet the demands
- Married women age Group 26-35 yrs from UP, Maharastra Karnataka working in IT, Healthcare and Aviation are more likely to buy products from Food, Clothing and Electronics category
- Diwali_Sales_Analysis.ipynb
- Performed data cleaning and manipulation
- Performed exploratery data analysis(EDA) using ggplot2, dplyr packeges
- Improved customer experience by identifying potential customers across different states, occupation, gender and age groups
- Improved sales by identifying most selling products categories and products, which can help to plan inventory and hence meet the demands
- Married women age Group 26-35 yrs from UP, Maharastra Karnataka working in IT, Healthcare and Aviation are more likely to buy products from Food, Clothing and Electronics category
- Ultra Marathon Running Project.ipynb
- Performed data cleaning and manipulation
- Performed exploratery data analysis(EDA)
- Analyzed the only USA Races, 50k or 50Mi, 2020
- Created new columns dropped unnecessary columns
- figured out that 50km, races ran by most peoples than 50miles, Males was more than in 50km, and 50mi race.
- Athlete_average_speed was around 7.5km, draw the linear model to find out that as the athlete_age goes up athlete_average_speed goes dowsn.
- Difference in speed for 50k, 50mi male to female male was on top from female
- What age groups are the best in the 50mi Race, 29 and 23 was best
- What age groups are the worst in the 50mi Race,The worst agr groups was 48, 50, 56, 59, 63, 64