Ishan Kotian
Ishan-Kotian
Data Scientist | Data & BI Analyst | Carlson MSBA Co'25 | Python • SQL • ML • Causal Inference • Tableau | Driving Impact with Analytics & Experimentation
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Hate Speech in the form of racism and sexism has become a nuisance on Twitter and it is important to segregate these sort of tweets from the rest. In this problem, I have taken the Twitter data that has both normal and hate tweets. So the task was to identify the tweets which have a positive connotation from the tweets that have a negative one.
The FIFA 19 dataset that has been used for this analysis provides statistics of about 18000 players on over 90 different attributes. These attributes are optimal indicators to determine the performance of a player at a particular playing position.
The goal is to make an Artificial Neural Network that can predict, based on geo-demographical and transactional information given above, if any individual customer will leave the bank or stay (customer churn). Also, rank all the customers of the bank, based on their probability of leaving.
Generative‑AI assistant that automates core equity research tasks from sourcing disclosures and news to synthesizing insights and drafting analyst‑ready output.
Business and financial case study of Procter & Gamble (P&G) analyzing strategy, financial performance, marketing mix, and operational recommendations with a focus on analytics-driven decision-making and sustainability.
Repositories
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Generative‑AI assistant that automates core equity research tasks from sourcing disclosures and news to synthesizing insights and drafting analyst‑ready output.
Business and financial case study of Procter & Gamble (P&G) analyzing strategy, financial performance, marketing mix, and operational recommendations with a focus on analytics-driven decision-making and sustainability.
Business and financial case study of Costco analyzing strategy, competitive position, and key financial ratios with recommendations for growth, e-commerce, and global expansion.
Business and financial case study analyzing Boeing’s strategy, 5C’s, 4P’s, and financial trends (2018–2023), including recommendations for operational recovery and market expansion.
Forecasted NASDAQ auction closing prices in the final 10 minutes using a stacked CatBoost–GRU–Transformer model. Achieved a MAE under $0.25/share, supporting short-term price signal detection for high-frequency trading simulations.
Assessed the return on investment of Bazaar.com's sponsored search ads using Difference-in-Differences regression on a natural experiment. Corrected flawed pre-post estimates by isolating true ad impact and generating a robust causal ROI metric.
Evaluated the effectiveness of Star Digital's display advertising campaign using randomized controlled trial data. Applied causal inference techniques to measure true ad impact while addressing attribution and conversion biases.
Analyzed two experiments using R to identify causal impacts of Reddit Gold on user engagement and the Balsakhi tutoring program on student test scores. Applied statistical tests and regression models to evaluate treatment effects and assess validity of causal assumptions.
This repository contains five predictive modeling projects covering classification and regression tasks, including cancer detection, car evaluation, and spending prediction. It also explores cost-sensitive spam filtering and compares shallow vs. deep neural networks for function approximation.
Image classification of cats vs. dogs using transfer learning with ResNet-50. Achieved ~98% accuracy using Keras and TensorFlow on the Kaggle Dogs vs. Cats dataset.
Spring Semester Trends Project
Life expectancy, an estimate of the number of remaining years of life a person has, is an important consideration for making clinical decisions in primary care. Predicting Life Expectancy helps analyze the average lifespan of the countrymen which helps in making crucial health decisions.
The FIFA 19 dataset that has been used for this analysis provides statistics of about 18000 players on over 90 different attributes. These attributes are optimal indicators to determine the performance of a player at a particular playing position.
Hate Speech in the form of racism and sexism has become a nuisance on Twitter and it is important to segregate these sort of tweets from the rest. In this problem, I have taken the Twitter data that has both normal and hate tweets. So the task was to identify the tweets which have a positive connotation from the tweets that have a negative one.
The goal is to make an Artificial Neural Network that can predict, based on geo-demographical and transactional information given above, if any individual customer will leave the bank or stay (customer churn). Also, rank all the customers of the bank, based on their probability of leaving.
Loan Prediction System Using Machine Learning - (ICACC-2022)
These datasets can be viewed as classification or regression tasks. The classes are ordered and not balanced (e.g. there are much more normal wines than excellent or poor ones).
If you liked my analysis, pls upvote my notebook!
If you liked my analysis, pls upvote my notebook!
If you liked my analysis, pls upvote my notebook!
If you liked my analysis, pls upvote my notebook!
Tokenization is a way of separating a piece of text into smaller units called tokens. Here, tokens can be either words, characters, or subwords. Hence, tokenization can be broadly classified into 3 types – word, character, and subword (n-gram characters) tokenization.
A neural network is a series of algorithms that endeavors to recognize underlying relationships in a set of data through a process that mimics the way the human brain operates. In this sense, neural networks refer to systems of neurons, either organic or artificial in nature. Neural networks can adapt to changing input; so the network generates the best possible result without needing to redesign the output criteria.