Machine Learning Project which predict the discounts on the products avalaible on Amazon and Flipkart This is the capstone project of Summer Analytics, a primer course on Data Science, conducted by Consulting and Analytics Club of IIT Guwahati. Description Artificial Intelligence is an integral part of all major e-commerce companies today. Today's online retail platforms are heavily powered by algorithms and applications that use AI. Machine learning is used in a variety of ways, from inventory control and quality assurance in the warehouse to product recommendations and sales demographics on the website. Let’s say you want to create a promotional campaign for an e-commerce store and offer discounts to customers in the hopes that this might increase your sales. You have been provided descriptions of products on Amazon and Flipkart, including details like product title, ratings, reviews, and actual prices. In this challenge, you will predict discounted prices of the listed products based on their ratings and actual prices.

This challenge is the capstone project of the Summer Analytics, a primer course on Data Science, conducted by Consulting and Analytics Club of IIT Guwahati in the summers. The dataset is provided by DeltaX is the pioneering cross-channel digital advertising platform. The cloud-based platform leverages big data, user behavior, and machine learning algorithms to improve performance across the business funnel of advertisers. Problem Statement Let's take a case where an advertiser on the platform (DeltaX) would like to estimate the performance of their campaign in the future. Imagine it is the first day of March and you are given the past performance data of ads between 1st August to 28th Feb. You are now tasked to predict an ad's future performance (revenue) between March 1st and March 15th. Well, it is now time for you to put on your problem-solving hats and start playing with the data provided under the "data" section.

project of Predictive Analytics of 【NUS Summer Workshop 2019, based on Celeb】

This is the practice project for the Text Analytics lecture at Nova Information Management School in summer semester of 2018.

A Data Analytics Website for the Automotive Industry. Part of the summer project for Microsoft Engage 2022.

The four-week summer component of Medlytics will take a deep-dive into the application of data analytics to structured data, physiological signals, and medical imagery. Prepared course material, case studies, and small-group projects will expose students to some of the challenges inherent to working with medical data and introduce them to state-of-the-art machine learning tools. Boston-area clinicians, academics, and industry leaders will visit the class to give daily guest seminars, providing valuable real-world knowledge and insights. 20 Students will compete in weekly challenges and participate in a final capstone project from concept proposal to live demonstration.

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Master repository for IIT Guwahati's Summer Analytics 2025 - Python data projects (Pandas/Seaborn/Scikit-learn) with documented solutions. Contains assignments, ML implementations, and visualizations from IITG's data science program

Slides for my presentation to the Strategic Data Project Summer Institute for Leadership in Analytics

A basic mean reversal trading strategy done as a part of summer project under Finance and Analytics Club IITK

A Capstone project for Summer Analytics 2025 by the Consulting & Analytics Club × Pathway. This project develops a dynamic pricing model for urban parking lots using data-driven analytics to optimize space utilization, reduce congestion, and maximize revenue.

a proof-of-concept mostly in Python for a potential data analytics project through Freifunk.de for Google Summer of Code 2024

📊 My IIT Guwahati Summer Internship 2025 analytics project. Focused on exploratory data analysis (EDA), data visualization, and real-world insights using Python and Jupyter.

# Tableau Homework - Citi Bike Analytics ### Before You Begin * This assignment will be saved to your tableau public account rather than github. * If you haven't already, be sure to create a tableau public account [here](https://public.tableau.com/s/). * The free tier of tableau only lets you save to their public server. This means that each time you save your file it will be uploaded to your tableau public profile. * You are able to load and continue working on the same workbook. * When you are finished with your assignment, you will turn in the URL to your tableau public workbook along with any additional files used for your analysis. ## Background  Congratulations on your new job! As the new lead analyst for the [New York Citi Bike](https://en.wikipedia.org/wiki/Citi_Bike) Program, you are now responsible for overseeing the largest bike sharing program in the United States. In your new role, you will be expected to generate regular reports for city officials looking to publicize and improve the city program. Since 2013, the Citi Bike Program has implemented a robust infrastructure for collecting data on the program's utilization. Through the team's efforts, each month bike data is collected, organized, and made public on the [Citi Bike Data](https://www.citibikenyc.com/system-data) webpage. However, while the data has been regularly updated, the team has yet to implement a dashboard or sophisticated reporting process. City officials have a number of questions on the program, so your first task on the job is to build a set of data reports to provide the answers. ## Task **Your task in this assignment is to aggregate the data found in the Citi Bike Trip History Logs and find two unexpected phenomena.** **Design 2-5 visualizations for each discovered phenomena (4-10 total). You may work with a timespan of your choosing. Optionally, you may merge multiple datasets from different periods.** **The following are some questions you may wish to tackle. Do not limit yourself to these questions; they are suggestions for a starting point. Be creative!** * How many trips have been recorded total during the chosen period? * By what percentage has total ridership grown? * How has the proportion of short-term customers and annual subscribers changed? * What are the peak hours in which bikes are used during summer months? * What are the peak hours in which bikes are used during winter months? * Today, what are the top 10 stations in the city for starting a journey? (Based on data, why do you hypothesize these are the top locations?) * Today, what are the top 10 stations in the city for ending a journey? (Based on data, why?) * Today, what are the bottom 10 stations in the city for starting a journey? (Based on data, why?) * Today, what are the bottom 10 stations in the city for ending a journey (Based on data, why?) * Today, what is the gender breakdown of active participants (Male v. Female)? * How effective has gender outreach been in increasing female ridership over the timespan? * How does the average trip duration change by age? * What is the average distance in miles that a bike is ridden? * Which bikes (by ID) are most likely due for repair or inspection in the timespan? * How variable is the utilization by bike ID? **Next, as a chronic over-achiever:** * Use your visualizations (does not have to be all of them) to design a dashboard for each phenomena. * The dashboards should be accompanied with an analysis explaining why the phenomena may be occuring. **City officials would also like to see one of the following visualizations:** * **Basic:** A static map that plots all bike stations with a visual indication of the most popular locations to start and end a journey with zip code data overlaid on top. * **Advanced:** A dynamic map that shows how each station's popularity changes over time (by month and year). Again, with zip code data overlaid on the map. * The map you choose should also be accompanied by a write-up unveiling any trends that were noticed during your analysis. **Finally, create your final presentation** * Create a Tableau story that brings together the visualizations, requested maps, and dashboards. * This is what will be presented to the officials, so be sure to make it professional, logical, and visually appealing. ## Considerations Remember, the people reading your analysis will **NOT** be data analysts. Your audience will be city officials, public administrators, and heads of New York City departments. Your data and analysis needs to be presented in a way that is focused, concise, easy-to-understand, and visually compelling. Your visualizations should be colorful enough to be included in press releases, and your analysis should be thoughtful enough for dictating programmatic changes. ## Submission Your final submission should include: * A link to your Tableau Public workbook that includes: * 4-10 Total "Phenomenon" Visualizations * 2 Dashboards * 1 City Official Map * 1 Story * A text or markdown file with your analysis on the phenomenons you uncovered from the data. ## Assessment Your final product will be assessed on the following metrics: * Analytic Rigor * Readability * Visual Attraction ## Hints * You may need to get creative in how you combine each of the CSV files. Don't just assume Tableau is the right tool for the job. At this point, you have a wealth of technical skills and research abilities. Dig for an approach that works and just go with it. * Don't just assume the CSV format hasn't changed since 2013. Subtle changes to the formats in any of your columns can blockade your analysis. Ensure your data is consistent and clean throughout your analysis. (Hint: Start and End Time change at some point in the history logs). * Consider building your visualizations with small extracts of the data (i.e. single files) before attempting to import the whole thing. What you will find is that importing all 20+ million records of data will create performance issues quickly. Welcome to "Big Data." * While utilizing all of the data may seem like a nice power play, consider the time-course in making your analysis. Is data from 2013 the most relevant for making bike replacement decisions today? Probably not. Don't let overwhelming data fool you. Ground your analysis in common sense. * Remember, data alone doesn't "answer" anything. You will need to accompany your data visualizations with clear and directed answers and analysis. * As is often the case, your clients are asking for a LOT of answers. Be considerate about their need-to-know and the importance of not "cramming in everything". Of course, answer each question, but do so in a way that is organized and presentable. * Since this is a project for the city, spend the appropriate time thinking through decisions on color schemes, fonts, and visual story-telling. The Citi Bike program has a clear visual footprint. As a suggestion, look for ways to have your data visualizations match their aesthetic tones. * Pay attention to labels. What exactly is "time duration"? What's the value of "age of birth"? You will almost certainly need calculated fields to get what you need. * Keep a close eye for obvious outliers or false data. Not everyone who signs up for the program is answering honestly. * In answering the question of "why" a phenomenon is occurring, consider adding other pieces of information on socioeconomic or other geographic data. Tableau has a map "layer" feature that you may find handy. * Don't be afraid to manipulate your data and play with settings in Tableau. Tableau is meant to be explored. We haven't covered all that you need -- so you will need to keep an eye out for new tricks. * Treat this as a serious endeavor! This is an opportunity to show future employers that you have what it takes to be a top-notch analyst. * Good luck! ### Copyright Data Boot Camp (C) 2019. All Rights Reserved.

Project for CSS661(Big Data Visual Analytics) for the Summer Term of 2024-2025.

Let's take a case where an advertiser on the platform (DeltaX) would like to estimate the performance of their campaign in the future. Imagine it is the first day of March and you are given the past performance data of ads between 1st August to 28th Feb. You are now tasked to predict an ad's future performance (revenue) between March 1st and March 15th. Well, it is now time for you to put on your problem-solving hats and start playing with the data provided under the "data" section

summer analytics course project

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Summer Analytics 2020 Capstone Project

A hackathon conducted on kaggle by the esteemed Consulting and Analytics group of IIT Guwahati as the last project of their 6 week long Summer Analytics course.

Dynamic Pricing for Urban Parking Lots. Capstone Project of Summer Analytics 2025 hosted by Consulting & Analytics Club × Pathway

Capstone project for Summer Analytics 2025 by Consulting & Analytics Club & Pathway

project of Predictive Analytics【NUS SUMMER workshop 2019】

Capstone project for Summer Analytics 2025 by IIT Guwahati

A prototype of a Student Performance Analytics Dashboard (summer project)

`Dynamic Pricing System for Urban Parking Lots - Summer Analytics 2025 Capstone Project`

Trail Trekker project for the Learn Analytics Engineering Data Pipeline Summer challenge.

This is the final capstone project for the Summer Analytics 2025 organized by Consulting & Analytics Club, IIT Guwahati

Summer Analytics 2025 Capstone Project – Dynamic pricing for urban parking lots using Pathway