Amena Saiyed

Committed job seeker with a history of meeting company needs with consistent and organized practices. Skilled in working under pressure and adapting to new situations and challenges to best enhance the organizational brand.

· Headed a fundraiser and gathered it for homeless shelter in COVID-19 pandemic.

· Awarded as THE CHAMPION OF CHANGE by Lions Club, Valsad,India,

· Being TCS(Tata Consultancy Services) campus ambassador

. Awarded as the Best student coordinator of Training & Placement Cell.

RFM (Recency, Frequency & Monetary) Customer Segmentation
Nov 2022

• Developed a model to segment customers based on RFM analysis using customer invoice data.
• Helped marketers target specific groups of customers, resulting in higher response rates, increased loyalty, and improved customer lifetime value.
• Utilized Python and machine learning techniques for data processing and model development.

Parking Management System
Jan 2022 - May 2022

• Designed an IoT-based parking system (SmartPark) to improve time efficiency and enhance user experience by allowing users to check the availability of parking slots via a mobile app.
• Built and maintained a Firebase database to manage user data and parking information in real-time.
• Implemented sensors and microcontrollers to monitor parking slot status, and update the mobile app

Stock Price Prediction Using Deep Learning
Jul 2023 - Oct 2023

• Built a stock price prediction model using deep learning techniques (LSTM) to forecast future stock prices based on historical data.
• Implemented data preprocessing, feature engineering, and model evaluation to optimize prediction accuracy.
• Used Python, Keras, and TensorFlow for model training and evaluation.

Sentiment Analysis on Stock News Using NLP
Apr 2024 - Jun 2024

• Developed a sentiment analysis model to assess stock market news articles using Natural Language Processing (NLP).
• Analyzed the sentiment of news headlines and correlated them with stock price movements to help investors make informed decisions.
• Implemented Python libraries such as NLTK, Scikit-learn, and Vader for text processing and sentiment classification.

Image Classification Using Convolutional Neural Networks (CNN)
Nov 2024- Dec 2024

Built an image classification model using CNNs to categorize images into different predefined classes (e.g., objects, animals).

• Used TensorFlow/Keras for building, training, and evaluating the model, achieving high classification accuracy on a public dataset.
• Applied image augmentation and optimization techniques to improve model robustness.