Logambal J's repositories
Smart-Predictive-Modeling-for-Rental-Property-Prices
The project employs data analysis and machine learning to predict rental prices accurately. By analyzing historical data and property features, the model aids , tenants, and property managers in pricing decisions. Through preprocessing, feature selection, and model training, the project delivers reliable rent predictions.
Students-Exam-Performance-Indicator
This project analyzes student performance based on demographics and educational factors, using machine learning to predict scores. It includes data cleaning, model training, and deployment of a web application for performance prediction. The project aims to provide insights for educators and policymakers to enhance student outcomes.
Term-Deposit-Subscription
This project uses predictive analytics to optimize marketing strategies by forecasting customer subscriptions to term deposits. It involves collecting and preprocessing data, training a model, and assessing its performance. Ongoing evaluation ensures adaptability to changing market dynamics, providing valuable insights for marketing analysis
BizCardX-Extracting-Business-Card-Data-with-OCR
The Streamlit application project aims to optimise the procedure of gathering and organising data from business cards. Python is used, along with easyOCR for optical character recognition and MySQL for data storage. To upload business card images, extract pertinent information, and manage data effectively, the application provides an intuitive GUI
Classifying-Medical-Conditions-from-Patient-Reviews-of-Drugs
This project aims to classify medical conditions based on patient reviews of drugs, leveraging natural language processing and machine learning techniques. By analyzing the text data from patient reviews, the goal is to develop a model that accurately predicts the medical conditions described in the reviews.
Data-Analysis-For-Optimizing-User-Engagement-App-and-Website-Installations
The assignment involves exploratory data analysis, descriptive analysis, and performance analysis of user installation and engagement, as well as an evaluation of past marketing campaigns. Recommendations will be proposed based on the analysis findings to optimize sales performance.
Data-Driven-Audience-Engagement-Optimization
Optimize audience engagement using data insights. Analyze content performance and audience behavior. Tailor strategies to resonate with target audience. Drive higher engagement and satisfaction levels
E-commerce-Customer-Segmentation
This project addresses the challenge of enhancing sales in e-commerce by using the k-means clustering algorithm to group customers based on factors like previous orders and brand searches. The dataset includes customer details and 35 brand search features.
Empowering-Sales-Strategy-Hardware-Business
This project aims to showcase a comprehensive data analysis project using Power BI, focusing on the challenges faced by a computer hardware business in a dynamically changing market. The project revolves around creating a Power BI dashboard to provide real-time sales insights to the Sales Director.
Exploring-Airbnb-Trends-with-MongoDB-Atlas-and-Streamlit
In order to obtain insights into pricing variations, availability patterns, and location-based trends, this project will use MongoDB Atlas to analyse Airbnb data, perform extensive data cleaning and preparation, develop interactive geospatial visualisations, and create dynamic plots.
Financial-Risk-Detection
This project uses EDA and machine learning to predict loan defaults, improving risk assessment for a finance company. Insights from historical loan data inform decision-making to minimize financial losses and ensure fair lending practices.
IMDB-Movie-Rating-Analysis
The goal is to investigate the factors that influence the success of a movie on IMDb. Success, in this context, is defined by high IMDb ratings. This analysis is crucial for movie producers, directors, and investors who aim to understand the key determinants of a movie's success and make informed decisions in their future projects.
Industrial-Copper-Modelling
Sales and pricing data that is subject to noise and skewness are managed with difficulty thanks to the Copper Industry Sales and Leads Prediction Project. In the industry, manual forecasts can be inaccurate and time-consuming. The creation of machine learning models is the main goal of this project in order to overcome these obstacles.
PhonePe-Pulse-Data-Visualization-Project
Millions of users use PhonePe, one of India's top digital payment platforms, on a daily basis. Based on different criteria like Years, Quarters, States, and Transaction Types, this web application offers a comprehensive analysis of PhonePe transactions. Providing a thorough understanding of transaction trends and user behaviors is the aim.
Rice-Exporter-Importer-Analysis
The purpose of this project is to perform a thorough data analysis in order to derive significant insights and answer important queries concerning rice export transactions.
YouTube-Data-Harvesting-and-Warehousing
Python scripting, data collection, MongoDB, Streamlit, API integration, and data management with SQL and MongoDB are the main areas of interest for this project. The project's objective is to create a Streamlit application that enables users to view and examine data from different YouTube channels.