Welcome to my Machine Learning Portfolio! This repository showcases a collection of machine learning projects in various domains, demonstrating my skills and expertise as a data scientist and machine learning engineer. Each project provides step-by-step instructions, code, and visualizations to showcase the data analysis and modeling techniques employed.
Welcome to my Machine Learning Portfolio! This repository showcases my skills and experience in the field of Machine Learning. Here, you will find a collection of projects and analyses that demonstrate my ability to extract insights and make data-driven decisions through Machine Learning.
- Project 1: Sales for Retail and Food Services
- Project 2: Wine Quality Prediction with MLFLOW experiments
- Project 3: Bank German Customer Segmentation & Classification Machine Learning Model as Flask API
- Project 4: New York Taxi Spending Prediction
- Project 5: Instagram Users Analytics
- Project 6: HINGLISH
- Project 7: Employee Distribution
- Project 8: Operational Analytics Study
- Project 9: Store Demand Forecasting
- Project 10: Product Recommendation
- Project 11: Hiring Process Analytics
In this project, I analyzed and visualized the sales data for Retail and Food Services in the U.S.A. The data is sourced from the U.S. government website and has been processed using SQL to create a database for easy management and analysis. The main focus of this project is to explore the sales data based on NAICS (North American Industry Classification System) code and category.
This project aims to predict the quality of wines using various machine learning algorithms. It utilizes the MLflow platform to manage the end-to-end machine learning lifecycle, including data preprocessing, model training, hyperparameter tuning, and deployment on AWS EC2.
This project aims to revolutionize the way banks market to their customers by leveraging machine learning techniques to segment customers based on their account history, credit scores, and demographics. By gaining insights into distinct customer groups, banks can tailor their marketing and customer acquisition strategies for optimal results.
Predict the average money spent on taxi rides for each region of New York per given day and hour. This problem is a supervised regression problem. Supervised because we have the actual value of the value we’re trying to predict and regression because what we’re trying to predict is a continuous variable (as opposed to categorical).
This project focuses on building a Neural Machine Translation (NMT) system to translate English sentences to Hindi. NMT has revolutionized the field of language translation by leveraging deep learning techniques to produce more accurate and natural-sounding translations.
This project focuses on building a Neural Machine Translation (NMT) system to translate English sentences to Hindi. NMT has revolutionized the field of language translation by leveraging deep learning techniques to produce more accurate and natural-sounding translations.
Exploring a treasure trove of insights and captivating visualizations drawn from a vast HR dataset covering 2000 to 2020, featuring over 22,000 records. Painstakingly curated and analyzed using PostgreSQL, this powerful dashboard showcases its findings through the elegance of Power BI. Unravel the secrets of the organization's workforce with answers to vital HR inquiries. Discover the gender and race/ethnicity breakdown, age distribution, headquarters vs. remote locations, average employment length for terminated employees, gender distribution across departments and job titles, job title distribution, highest turnover department, state-wise employee distribution, and changes in employee count over time based on hire and termination dates. Experience a profound understanding of employee tenures across departments. Employee-Distribution delivers valuable HR insights at your fingertips.
The project will cover a wide range of areas, including user engagement, sales performance, marketing effectiveness, and more. You'll be provided with access to datasets containing user information, events data, and email engagement details.
This project predicts the sales demand for various items in different stores based on historical sales data. The objective is to develop a machine learning model that can provide accurate forecasts for future sales of each store-item combination.
E-commerce companies are relentlessly working to augment user experiences and bolster sales by offering tailor-made product recommendations. The "E-commerce Product Recommendation" project endeavors to create a recommendation system that suggests products to users based on their historical interactions and preferences.
In this project, I am analyzing hiring process data to gain insights from about records of previous hires within a multinational company. By analyzing this data, I am aiming to uncover valuable trends and information about the company's hiring process, which can contribute to making informed decisions and improvements for the future.
If you have any questions, feedback, or collaboration opportunities, please feel free to reach out to me. You can contact me via email at info@tushar-aggarwal.com or connect with me on LinkedIn at Tushar Aggarwal.
Thank you for visiting my Data Analysis Portfolio! I hope you find my projects informative and insightful.