AhmedHamdy2b/visualizing-stocks

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Python
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GNU General Publi...
Created over 3 years ago
Updated over 3 years ago

AhmedHamdy2b/visualizing-stocks

AhmedHamdy2b

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Applying Data Visualization Techniques for Stock Relationship Analysis Using Some Library Like mpl-finance

gender-predection-py-voice

About Dataset Voice Gender Gender Recognition by Voice and Speech Analysis This database was created to identify a voice as male or female, based upon acoustic properties of the voice and speech. The dataset consists of 3,168 recorded voice samples, collected from male and female speakers. The voice samples are pre-processed by acoustic analysis in R using the seewave and tuneR packages, with an analyzed frequency range of 0hz-280hz (human vocal range). The Dataset The following acoustic properties of each voice are measured and included within the CSV: meanfreq: mean frequency (in kHz) sd: standard deviation of frequency median: median frequency (in kHz) Q25: first quantile (in kHz) Q75: third quantile (in kHz) IQR: interquantile range (in kHz) skew: skewness (see note in specprop description) kurt: kurtosis (see note in specprop description) sp.ent: spectral entropy sfm: spectral flatness mode: mode frequency centroid: frequency centroid (see specprop) peakf: peak frequency (frequency with highest energy) meanfun: average of fundamental frequency measured across acoustic signal minfun: minimum fundamental frequency measured across acoustic signal maxfun: maximum fundamental frequency measured across acoustic signal meandom: average of dominant frequency measured across acoustic signal mindom: minimum of dominant frequency measured across acoustic signal maxdom: maximum of dominant frequency measured across acoustic signal dfrange: range of dominant frequency measured across acoustic signal modindx: modulation index. Calculated as the accumulated absolute difference between adjacent measurements of fundamental frequencies divided by the frequency range label: male or female

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AhmedHamdy2b

therapy-chatbot

The Therapy Chatbot is a Python-based chatbot that provides a conversational interface for users to discuss their feelings and receive appropriate responses. It uses a combination of natural language processing techniques and deep learning to understand user input and generate relevant replies.

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music-generation-RNN-LSTM

The music-generation-RNN-LSTM project uses Python and TensorFlow to generate new music using LSTM models that learn from a large dataset of MIDI files. It's a creative and innovative application of machine learning techniques to the field of music generation.

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web-scrabing-for-mostactive-stocks

wine-quality-randomforest-gridsearch

The "Wine Quality Prediction using Random Forest and Grid Search" project is a machine learning project focused on analyzing and predicting the quality of wines based on various chemical properties. This project optimization technique to develop a robust and accurate predictive model.

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House-Prices---Advanced-Regression-Techniques

Predict sales prices and practice feature engineering, RFs, and gradient boosting

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malware-detection-using-machine-learning

Malware are considered as the malicious software program which infects the normal working of the PCs or Mobile. The concept of machine learning is utilized in the detection of the malwares, various sub-approaches like the ANN, CNN etc. are used by various researchers for the detection purpose. In this project, we explore the various different types of the models which are used by various researchers in the malware detection thereby highlighting the accuracy of these models.

edith-voice-assistant

A EDITH is a digital assistant that uses voice recognition, language processing algorithms, and voice synthesis to listen to specific voice commands and return relevant information or perform specific functions as requested by the user. Based on specific commands, sometimes called intents, spoken by the user, EDITH can return relevant information by listening for specific keywords and filtering out the ambient noise.

Stock-Prediction

Stock market prediction is the act of trying to determine the future value of a company stock or other financial instrument traded on an exchange. The successful prediction of a stock's future price could yield significant profit. The efficient-market hypothesis suggests that stock prices reflect all currently available information and any price changes that are not based on newly revealed information thus are inherently unpredictable. Others disagree and those with this viewpoint possess myriad methods and technologies which purportedly allow them to gain future price information by using tree & linear methods

Alzheimer-detection-and-classification

Alzheimer MRI Preprocessed Dataset (128 x 128) The Data is collected from several websites/hospitals/public repositories. The Dataset is consists of Preprocessed MRI (Magnetic Resonance Imaging) Images. All the images are resized into 128 x 128 pixels. The Dataset has four classes of images. The Dataset is consists of total 6400 MRI images. Class - 1: Mild Demented (896 images) Class - 2: Moderate Demented (64 images) Class - 3: Non Demented (3200 images) Class - 4: Very Mild Demented (2240 images) Motive The main motive behind sharing this dataset is to design/develop an accurate framework or architecture for the classification of Alzheimers Disease.

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