Embedded Files
Project Description:
The SMS Spam Detection App is a machine learning-based solution designed to classify SMS messages as Spam or Ham (Not Spam) in real time. Leveraging Natural Language Processing (NLP) techniques like TF-IDF vectorization and a supervised machine learning model, the app ensures accurate and efficient classification.
Built with Python, the app features a user-friendly interface powered by Flask or Streamlit, making it accessible to both technical and non-technical users. The app processes raw SMS data, cleans and tokenizes it, and predicts the category of messages with high precision.
This project showcases expertise in Python programming, data preprocessing, and machine learning model deployment, while emphasizing scalability and practical application in combating spam.
Key Features:
Real-time message classification.
Lightweight and optimized for performance.
Interactive interface for ease of use.
The app aims to enhance communication by reducing spam messages, making it a valuable tool for users and organizations alike.
Technologies Used: Python, scikit-learn, Pandas, Flask/Streamlit.
Project PPD :
Project PPD :
AICTE_Internship_2024_Project_Report_Template_2(anjha).pdf💡 Click the link below to explore the project. The website will load in 1 minute, so please wait for the system to initialize.
Predicting Forest Fires using AutoML and AutoAI
Project Description:
In this project, I utilized IBM Watson Studio's AutoAI to predict forest fires, leveraging historical and environmental data. AutoAI automated the creation of machine learning pipelines and regression models, streamlining development. Key results include a Linear Regression model with an R2 Score of 0.9830 and a Mean Absolute Error of 0.5806. This project demonstrates the feasibility of using machine learning for disaster management. Future enhancements involve expanding the dataset and integrating real-time data for a comprehensive early warning system.
Project PPT :
Project PPT :
Alpha-Thalassemia-Classifier-A-Machine-Learning-Approach
Project Description:
The project "Alpha Thalassemia Classifier" is a machine learning model developed to classify individuals based on clinical and blood parameter data to identify alpha-thalassemia. The classifier uses various blood parameters to predict whether an individual is a carrier or affected by the condition. The dataset includes features like sex, hemoglobin level, red blood cell count, white blood cell count, and several other hematological factors. The model is trained using Python with libraries like Scikit-learn and Flask for web deployment. The system aims to assist in the early diagnosis of alpha-thalassemia based on clinical blood data.
Project PDF :
Project PDF :
Anjha_Assign_ML.pdfAlpha_Thalassemia_Classifier Parameters Descriptions.pdfThis project addresses the issue of employee burnout using machine learning and data analysis techniques. Burnout is a critical organizational challenge that impacts productivity and morale. Using a dataset from Kaggle, features such as designation, resource allocation, and mental fatigue score were analyzed to predict an employee's burn rate—a numerical indicator of burnout risk.
A regression model was developed using Python, leveraging libraries like Pandas, NumPy, and Scikit-learn. The model was deployed via a Flask web application, enabling users to input data and view predictions in real-time through the URL: Employee Burnout Prediction.
The project demonstrated accurate predictions, allowing organizations to identify and support high-risk employees. Future improvements include adding more features, interactive dashboards, and integration with HR systems to better manage employee well-being.
Project Description:
The Tic Tac Toe project is a classic game application that allows two players to compete in a grid-based strategy game. This project features an intuitive interface, enabling players to take turns marking their symbols (X or O) on a 3x3 grid. The game automatically detects winning conditions and announces the winner or a draw. It is designed for both beginners and experienced players, providing an engaging and interactive gaming experience.
Project PPT :
Project PPT :
The Weather App is a user-friendly application that provides real-time weather updates, including temperature, humidity, wind speed, and precipitation. Designed for daily use, it helps users stay informed about current weather conditions and forecasts, ensuring they can plan their activities accordingly. The app features an intuitive interface and accurate data to deliver a seamless user experience.
Project PPT :
Project PPT :
DevDetective for GitHub is a web application that allows users to quickly access detailed information about GitHub profiles. By entering a GitHub username, users can view profile details, skills, repository count, and social insights, making it a valuable tool for exploring developers' contributions and expertise.
Analysis of Delivery Partners and Vehicle Costs
Project Description:
The Vehicle Fleet Management and Optimization project aims to enhance the efficiency and sustainability of a company's fleet operations. By analyzing vehicle utilization, fuel costs, and maintenance schedules, the project identifies key areas for cost reduction and performance improvement. The project employs data analytics to assess each vehicle's fuel efficiency and maintenance needs, leading to informed decisions about fleet composition and usage patterns.
Recommendations focus on adopting more fuel-efficient vehicles, optimizing maintenance schedules, and implementing best practices for vehicle use, ultimately reducing operational costs and environmental impact. This project provides actionable insights to streamline fleet management and promote long-term sustainability.
Project PPT :
Project PPT :
Tata Data Visualisation: Empowering Business with Effective Insights Job Simulation on Forage
Completed a simulation involving creating data visualizations for Tata Consultancy Services
Prepared questions for a meeting with client senior leadership
Created visuals for data analysis to help executives with effective decision making
Quantium Data Analytics Job Simulation on Forage
Completed a job simulation focused on Data Analytics and Commercial Insights for the data science team.
Developed expertise in data preparation and customer analytics, utilizing transaction datasets to extract valuable insights and deliver data-driven commercial recommendations.
Extended analytical capabilities to identify benchmark stores for conducting uplift testing on trial store layouts, enabling evidence-based decision-making.
Leveraged acquired data analytics and insights from previous tasks to create comprehensive reports for the Category Manager, facilitating informed strategic decisions and enhancing commercial applications.
PwC Switzerland Power BI Job Simulation on Forage
Completed a job simulation where I strengthened my PowerBI skills to better understand clients and their data visualisation needs.
Demonstrated expertise in data visualization through the creation of Power BI dashboards that effectively conveyed KPIs, showcasing the ability to respond to client requests with well-designed solutions.
Strong communication skills reflected in the concise and informative email communication with engagement partners, delivering valuable insights and actionable suggestions based on data analysis.
Leveraged analytical problem-solving skills to examine HR data, particularly focusing on gender-related KPIs, and identified root causes for gender balance issues at the executive management level, highlighting a commitment to data-driven decision-making.
AWS APAC Solutions Architecture virtual experience program on Forage
Designed and simple and scalable hosting architecture based on Elastic Beanstalk for a client experiencing significant growth and slow response times.
Described my proposed architecture in plain language ensuring my client understood how it works and how costs will be calculated for it.
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