Existing precipitation prediction models have high error rates. The goal of this research is to reduce the error rates of the existing prediction models. An ensemble approach has been proposed to develop a New Aggregated Model to predict precipitation based on the dataset of some existing prediction models. This is a part of my master's thesis project.
Optimized hyperparameters in a Random Forest model (RFM) to achieve a certain error tolerance (MSE criterion) using Python
Model Evaluation and Validation Project: Predicting Boston Housing Prices Install This project requires Python and the following Python libraries installed: NumPy Pandas matplotlib scikit-learn You will also need to have software installed to run and execute a Jupyter Notebook If you do not have Python installed yet, it is highly recommended that you install the Anaconda distribution of Python, which already has the above packages and more included. Code Template code is provided in the boston_housing.ipynb notebook file. You will also be required to use the included visuals.py Python file and the housing.csv dataset file to complete your work. While some code has already been implemented to get you started, you will need to implement additional functionality when requested to successfully complete the project. Note that the code included in visuals.py is meant to be used out-of-the-box and not intended for students to manipulate. If you are interested in how the visualizations are created in the notebook, please feel free to explore this Python file. Run In a terminal or command window, navigate to the top-level project directory boston_housing/ (that contains this README) and run one of the following commands: ipython notebook boston_housing.ipynb or jupyter notebook boston_housing.ipynb This will open the Jupyter Notebook software and project file in your browser. Data The modified Boston housing dataset consists of 489 data points, with each datapoint having 3 features. This dataset is a modified version of the Boston Housing dataset found on the UCI Machine Learning Repository. Features RM: average number of rooms per dwelling LSTAT: percentage of population considered lower status PTRATIO: pupil-teacher ratio by town Target Variable 4. MEDV: median value of owner-occupied homes
Implementation of High Quality Protein Q8 Secondary Structure Prediction by Diverse Neural Network Architectures
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BEST SCORE ON KAGGLE SO FAR. Mean Square Error after repeated tuning 0.00032. Used stacked GRU + LSTM layers with optimized architecture, learning rate and batch size for best model performance. The graphs are self explanatory once you click and go inside !!!
Convolutional LSTM neural network to extrapolate radar images, and predict rainfall - CIKM 2017 contest
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Utilizing the ensemble method of random forests to predict stock prices.
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NYC taxi trip prediction using Advanced ensemble models. I used XGB , averaging and stacking ensemble models.
Generative Adversarial Network for the generation of historical stock price data
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Implementation of stock technical indicators and deep LSTM for closing price 30-day lookahead predictions.
