A hybrid recommendation system for Sephora was developed, combining collaborative and content-based filtering. It involved the utilization of NLP for sentiment analysis on user reviews to enhance user satisfaction and drive sales.
This project focuses on stock price prediction and forecasting by implementing a stacked Long Short-Term Memory (LSTM) neural network. The goal is to create a robust model for predicting stock prices, leveraging the power of deep learning techniques to enhance decision-making in financial markets.
A chatbot designed to facilitate document inquiry. Utilizing state-of-the-art machine learning algorithms, it can understand and respond to user queries, providing precise and relevant information extracted from documents. Whether it’s finding specific information within a document, searching for documents containing certain keywords, or even summarizing the content of a document, it makes document inquiry seamless and efficient.
Effortlessly transformed spoken English into Hindi voice. Leveraged Automatic Speech Recognition (ASR), translation, and Text-to-Speech (TTS) technologies. This project served as a bridge between languages, showcasing the power of technology to enhance communication and understanding. It involved a meticulous process of data preprocessing, algorithm fine-tuning, and collaboration with experts in the field.
Car prices are predicted using various machine learning algorithms and techniques. A rigorous process of data cleaning, exploratory data analysis (EDA), feature engineering, data preprocessing, model development, and evaluation are undertaken to arrive at best-performing model, the Random Forest Regressor.
An exploration and visualization of the IMDB movie database has been conducted using Power BI in this project. Insights into movie ratings, genres, and trends over time are provided. The data can be filtered and drilled down into by users through the interactive dashboards.
In this project, Natural Language Processing (NLP) techniques are utilized to detect spam in emails. A machine learning model is trained on a dataset of spam and non-spam emails, enabling the system to classify incoming emails accurately as either spam or not. This can facilitate the automation of filtering out unwanted emails.
Data from Zomato is meticulously examined using SQL (Structured Query Language). The process involves the extraction, cleansing, and analysis of data from Zomato’s extensive database to uncover patterns in customer behavior, restaurant performance, and food trends. This includes the scrutiny of customer reviews, ratings, location data, among other things.
It is a comprehensive project that leverages historical advertisement data to predict future sales. By employing statistical and machine learning methodologies, the model analyzes past sales and advertisement data to discern patterns and correlations. These insights are then utilized to construct predictive models capable of forecasting future sales under varying advertisement conditions.
A thorough analysis of employee attrition is conducted. The objective of this project is to predict the likelihood of employee attrition based on various factors such as job satisfaction, work-life balance, salary, years at the company, and more. Machine learning algorithms are employed to build a predictive model that can accurately forecast employee turnover.
