The main goal of this project is to develop a CNN model capable of accurately classifying house numbers in the Street View House Numbers (SVHN) dataset. The SVHN dataset consists of images containing house numbers from real-world scenarios, such as street addresses, storefronts, and building exteriors.It enables accurate address recognition, automates data entry tasks, aids in urban planning and infrastructure development, provides insights into urban environments, improves navigation systems, enhances operational efficiency, and promotes accessibility and inclusion.
Read More ->
This project aims to classify the quality of wines using Self-Organizing Maps (SOM), an unsupervised machine learning algorithm. The dataset used for this analysis is the "Red Wine Quality" dataset sourced from the UCI Machine Learning Repository. The dataset contains various physicochemical properties of red wines, along with their quality ratings provided by experts.
Read More ->
This project aims to develop a predictive model using machine learning techniques to accurately predict the presence or likelihood of diabetes in individuals. Diabetes is a chronic condition affecting a significant portion of the global population, and early detection plays a vital role in managing and preventing complications. By leveraging advanced machine learning algorithms and analysis of various health-related features, I aim to build a robust and accurate model that can assist in early identification and intervention for individuals at risk of developing diabetes.
Read More ->
The proliferation of fake news in the digital era poses significant challenges to information credibility and public trust. This project aims to develop a machine learning model for detecting and predicting fake news. By leveraging advanced natural language processing techniques and various features extracted from news articles and related data, I strive to build a robust model capable of distinguishing between genuine and deceptive information.
Read More ->
Stock market prediction has always been a challenging task due to its complex and volatile nature. However, with advancements in machine learning and deep learning techniques, it has become possible to develop predictive models that can capture patterns and dependencies in stock price data. In this project, we aim to utilize Recurrent Neural Networks (RNN) to forecast the stock prices of Google (GOOGL) from 2012 to 2017.
The goal of the project is to develop a machine learning model capable of accurately recognizing and classifying these handwritten digits. By training a neural network on the MNIST dataset, I aim to build a model that can generalize well and correctly classify new, unseen handwritten digits.
Read More ->
This project aims to improve an understanding of the factors affecting life expectancy on a county level in the United States. The analysis builds upon the paper "The Association between Income and Life Expectancy in the United States, 2001-2014," which highlights the strong relationship between socioeconomic background and health outcomes.
Read More ->
In this research, I will examine a Salaried for Professor data set in the "car" R package to demonstrate the influence of years in services with gender control on the salary of Assistant Professors, Associate Professors, and Professors in the US college. I expect the finding will demonstrate that tenured professors with a high year in service correspond to high salaries.
Read More ->
The Spam and Ham Filtering project aims to develop a machine learning model that can accurately classify incoming messages as either "spam" or "ham" (non-spam). By utilizing the Multinomial Naive Bayes algorithm and text processing techniques, this project focuses on creating an efficient and effective system for filtering unwanted and potentially harmful messages.
Read More ->
The goal of this project is to develop a machine learning model using Recurrent Neural Networks (RNNs) to accurately classify text messages as either spam or ham (non-spam). The rise of unsolicited and unwanted messages has made spam filtering a crucial task in ensuring an efficient communication experience.
Read More ->
The Car Price Prediction project aims to develop a machine learning model that can accurately predict the price of cars based on various features and attributes. By utilizing the power of Linear Regression and Lasso regularization techniques, this project focuses on creating a reliable system for estimating car prices and assisting buyers and sellers in making informed decisions.
Read More ->
The aim of this project is to develop a machine learning model that predicts housing prices in California based on the California housing dataset. The dataset contains various features related to houses in different locations across California, such as median income, house age, average number of rooms and bedrooms, population, and geographical coordinates.
Read More ->
The objective of this project is to develop a machine learning model that predicts the loan status of individuals using the Support Vector Machine (SVM) algorithm. The model will be trained on a loan dataset obtained from Kaggle, which contains various features related to loan applicants. By analyzing these features, the SVM model will classify loan applications as either approved or rejected.
Read More ->
The heart disease prediction project aims to develop a machine learning model capable of accurately predicting the presence or absence of heart disease in patients. Heart disease is a prevalent and potentially life-threatening condition, making early detection and intervention crucial for effective treatment and prevention. Through the application of machine learning algorithms and data analysis techniques, the project aims to uncover patterns, relationships, and risk factors associated with heart disease.
Read More ->