Here are Some of my Projects

ANALYSE HISTORICAL IPL DATA AND PROVIDE INSIGHTS ON IPL 2024 FOR A SPORTS MAGAZINE (POWERBI)

"Sports Basics" is a sports blog company that entered space recently. They wanted to get more traffic to their website by releasing a special edition magazine on IPL 2024. This magazine aims to provide interesting insights and facts for fans, analysts and teams based on the last 3 years' data.The chief editor Tony Sharma oversees this publication, and he believes in data analytics. He reached out to Peter Pandey, a journalist in his team who is a data savvy cricket enthusiast.Task:
1.Imagine yourself as Peter Pandey and perform the following task.
2.Begin your analysis by referring to the‘primaryandsecondary_analysis.pdf’. You can use any tool of your choice (Python, SQL, PowerBI, Tableau, Excel, PowerPoint) to analyze and answer these questions.
3.Design a dashboard with your metrics and analysis. The dashboard presents a concise summary of players and team performance over the past three years.

• View the project here

CREDIT RISK CASE USING EXPLORATORY DATA ANALYSIS (EDA,PANDAS)

The loan providing companies find it hard to give loans to people due to their inadequate or missing credit history.Some consumers use this to their advantage by becoming a defaulter.When the company receives a loan application, the company has to rights for loan approval based on the applicant’s profile.
Two types of risks are associated with the bank’s or company’s decision:
--If the aspirant is likely to repay the loan, then not approving the loan tends in a business loss to the company.
--If the a is aspirant not likely to repay the loan, i.e. he/she is likely to default/fraud, then approving the loan may lead to a financial loss for the company.
The data contains information about the loan application.
When a client applies for a loan, there are four types of decisions that could be taken by the bank/company:
Approved
Cancelled
Refused
Unused offerBusiness Goal
This case study aims to identify patterns that indicate if an applicant will repay their instalments which may be used for taking further actions such as denying the loan, reducing the amount of loan, lending at a higher interest rate, etc. This will make sure that the applicants capable of repaying the loan are not rejected. Recognition of such aspirants using Exploratory Data Analysis (EDA) techniques is the main focus of this case study.

• View the project here

Promotion Analysis and Insights for Sales Director (Powerbi ,SQL)

AtliQ Mart is a retail giant with over 50 supermarkets in the southern region of India. All their 50 stores ran a massive promotion during the Diwali 2023 and Sankranti 2024 (festive time in India) on their AtliQ branded products. Now the sales director wants to understand which promotions did well and which did not so that they can make informed decisions for their next promotional period.Sales director Bruce Haryali wanted this immediately but the analytics manager Tony is engaged on another critical project. Tony decided to give this work to Peter Pandey who is the curious data analyst of AtliQ Mart. Since these insights will be directly reported to the sales director, Tony also provided some notes to Peter to support his work.Task:Imagine yourself as Peter Pandey and perform the following task to keep up the trust with your manager Tony Sharma.Go through the metadata and analyze the datasets thoroughly. This is the most fundamental step.
Check “Recommended Insights.pdf” – this document contains a few recommendations from your manager Tony.
Design a dashboard with your metrics and analysis. The dashboard should be self-explanatory and easy to understand.
Check “ad-hoc-requests.pdf” - this document includes important business questions posed by senior executives, requiring SQL-based report generation.
You need to present this to the sales director - hence you need to create a convincing presentation with actionable insights.
You can add more research questions and answer them in your presentation that suits your recommendations.

• View the project here

Store sales Data Analysis (Excel)

This project entails an in-depth analysis of Vrinda Store's data to facilitate informed decisions aimed at enhancing sales performance. The dataset provided has undergone rigorous data cleaning, processing, analysis, and finally, dashboard creation, utilizing Excel as the primary tool.The primary objective of this project is to extract meaningful insights from Vrinda Store's data, allowing stakeholders to identify trends, patterns, and areas for improvement. By leveraging data-driven decision-making, the aim is to devise strategies that can effectively boost sales and optimize business operations.
Insights
>Women lead the charge in our sales, contributing an impressive 65% of purchases.>Maharashtra, Karnataka, and Uttar Pradesh are the top 3 states, driving approximately 35% of our total sales!>The age group between 30-49 years proves to be the key contributors, making up a significant 50% of our sales!>Amazon, Myntra, and Flipkart take center stage, contributing a substantial 80% towards our overall sales success!Conclusionwe should run promotional discounts,more ads,mainly on women section area having age in mid range on platforms like Amazon,Myntra,Flipkart so that we can boost out sales.

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cognifyz Technologies-Internship project

The dataset encompasses different types of investments, including mutual funds, equity bonds, and PPFs. Additionally, it provides details on the sources of information for these investments, as well as demographic information such as gender and age.Objectives:
--Understand and summarize key statistics in the dataset.
--Analyze and visualize gender-based differences in investment preferences.
--Analyze the relationship between savings objectives and investment choices.
--Analyze investment durations and monitoring frequencies.
--Analyze and visualize the reasons for investment.
--Analyze the sources from which individuals gather investment information.

• View the project here