python code for real estate price prediction on timeseries dataset by encoder decoder transformer with attention

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I understand that you are looking for a Python programmer to develop a python code that will help predict the future real estate prices using timeseries datasets. This project will primarily involve the use of encoder-decoder-transformers with attention which is an advanced form of deep learning.

My name is Vishruth and I am a highly experienced Python programmer with 4 years of experience in software development, Machine Learning, Automation, Integration. I have the necessary expertise and qualifications to help you develop a python code that will help predict the future real estate prices using timeseries datasets. Specifically, I would use encoder-decoder-transformers with attention an advanced form of deep learning. I understand that this project involves a large amount of data with more than 10,000 data points and requires extensive knowledge in real estate prices, price history and other related features. As such, if you choose me to complete this project please don't hesitate to get in touch with me so we can discuss further.

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I am Grace, your data analyst with 4 years of experience in the fields of data science and Python. I understand your need for a python code that will help predict future real estate prices using timeseries datasets. My expertise includes working with deep learning neural networks such as encoder-decoder-transformers with attention. Furthermore, a knowledge of real estate prices, price history and other related features is necessary for this project. I have the necessary skills and qualifications to complete this project. If you would like me to discuss further please feel free to reach out to me directly. Thank you for considering me for this position!

I understand that you are looking for a Python programmer to develop a python code that will help predict the future real estate prices using timeseries datasets. This project will primarily involve the use of encoder-decoder-transformers with attention which is an advanced form of deep learning. My skillset includes extensive experience in working with deep learning neural networks such as encoder-decoder-transformers with attention. Additionally, I have good knowledge of real estate prices, price history and other related features necessary for this project. This makes me the perfect fit for this project as I can deliver quality-driven bug-free websites and mobile applications using technologies such as HTML, CSS, WordPress, PHP (Magento), JavaScript (MEAN+MERN) and more. I am available to work on this project immediately and would be glad to discuss further if necessary. Please feel free to contact me if you want more information or want to discuss this project further..

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I understand that you are looking for a Python code that will help predict the future real estate prices using timeseries datasets. This project will primarily involve the use of encoder-decoder-transformers with attention which is an advanced form of deep learning. My skillset includes strong experience in working with deep learning neural networks such as encoder-decoder-transformers with attention. Additionally, I have a knowledge of real estate prices, price history and other related features required for this project. This makes me the perfect candidate for this job and I would be delighted to help you out with your requirements. Please feel free to contact me if you have any further questions or would like to discuss further regarding this project.

Hello, I'm eager to work on your real estate price prediction project using time series datasets and advanced deep learning techniques, particularly encoder-decoder-transformers with attention. This is a complex and challenging project, and I'm confident in my ability to meet your requirements. Here's how I plan to contribute to your project: 1. Deep Learning Expertise: I have extensive experience working with deep learning neural networks, including encoder-decoder-transformers with attention. This expertise is vital for accurate price predictions. 2. PyTorch Proficiency: I will use PyTorch as the Python library/framework for this project, ensuring efficient model development and training. 3. Large Dataset Handling: I'm well-equipped to handle large datasets with over 10,000 data points, which is essential for robust predictions. 4. Price History Utilization: I will incorporate price history as a feature for predicting real estate prices, enhancing the model's accuracy. 5. Real Estate Knowledge: My background in real estate prices and related features enables me to create a model that aligns with the real estate market dynamics. I'm ready to take on this challenging project and deliver accurate real estate price predictions. If you have specific project details or requirements to discuss, please don't hesitate to reach out. I'm confident that my skills and expertise are well-suited for this task. Best regards, Divyang Mandani Founder KriraAI Infotech

I understand that you are looking for a freelancer to develop a python code that will help predict the future real estate prices using timeseries datasets. This project will primarily involve the use of encoder-decoder-transformers with attention which is an advanced form of deep learning.

Hello, I am an expert software engineer with 4+ years of experience. I have written a lot of programs in Python and MATLAB, for various domains like ML, signal processing, image processing, basic scripting etc. I have also converted codes both ways(MATLAB to Python and Python to MATLAB) various times for many projects. I know exactly how each function/operation in matlab is converted to python. I can easily complete this job quickly. Price can be negotiated depending on the complexity and length of the codes. Hope to hear from you

Hello there, having previously worked on similar price predictions, I think this can be a pretty straightforward situation, I'll give you a skeleton approach that would work in this scenario for you, if you think that is a good starting point for you, hit me up. Technical Approach: Data Preparation: Data Cleaning: Address missing values and outliers. Feature Engineering: Emphasize historical price trends. Dataset Split: Create training, validation, and testing sets. Model Architecture: Encoder-Decoder Transformers with Attention. Hyperparameter Optimization: Tune model parameters for optimal performance. Training: Loss Function: Minimize Mean Squared Error (MSE). Regularization: Prevent overfitting using dropout and weight decay. Batch Processing: Efficient handling of the large dataset. Evaluation and Validation: Validation Set: Monitor model performance during training. Cross-Validation: Ensure model robustness. Metrics: Assess accuracy using RMSE, MAE, and R-squared. Testing and Deployment: Assess model generalization on test data. Deploy with an API for real-time predictions. Implement model monitoring to detect drift. Domain Expertise: Leverage real estate knowledge for improved model accuracy. Utilize historical insights for feature selection and training.