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Gender Recognition by Voice (processed)

help identifying male and female voice

Features:

The dataset includes the following extracted audio features:

mean_spectral_centroid: The average spectral centroid, representing the "center of mass" of the spectrum, indicating brightness.
std_spectral_centroid: The standard deviation of the spectral centroid, measuring variability in brightness.
mean_spectral_bandwidth: The average width of the spectrum, reflecting how spread out the frequencies are.
std_spectral_bandwidth: The standard deviation of spectral bandwidth, indicating variability in frequency spread.
mean_spectral_contrast: The average difference between peaks and valleys in the spectrum, indicating tonal contrast.
mean_spectral_flatness: The average flatness of the spectrum, measuring the noisiness of the signal.
mean_spectral_rolloff: The average frequency below which a specified percentage of the spectral energy resides, indicating sharpness.
zero_crossing_rate: The rate at which the signal crosses the zero amplitude axis, representing noisiness or percussiveness.
rms_energy: The root mean square energy of the signal, reflecting its loudness.
mean_pitch: The average pitch frequency of the audio.
min_pitch: The minimum pitch frequency.
max_pitch: The maximum pitch frequency.
std_pitch: The standard deviation of pitch frequency, measuring variability in pitch.
spectral_skew: The skewness of the spectral distribution, indicating asymmetry.
spectral_kurtosis: The kurtosis of the spectral distribution, indicating the peakiness of the spectrum.
energy_entropy: The entropy of the signal energy, representing its randomness.
log_energy: The logarithmic energy of the signal, a compressed representation of energy.
mfcc_1_mean to mfcc_13_mean: The mean of the first 13 Mel Frequency Cepstral Coefficients (MFCCs), representing the timbral characteristics of the audio.
mfcc_1_std to mfcc_13_std: The standard deviation of the first 13 MFCCs, indicating variability in timbral features.
label: The target variable indicating the gender male(1) or female(0).

https://news.1rj.ru/str/datasets1

Lightening Strikes Dataset NOAA

2018 lightning strike data by National Oceanic and Atmospheric Administration

Dataset Denoscription: NOAA Lightning Strikes Dataset
The NOAA (National Oceanic and Atmospheric Administration) Lightning Strikes dataset provides insights into lightning activity over a given region or time period. This dataset is a product of NOAA's weather monitoring and storm tracking systems, offering valuable information for meteorologists, researchers, and disaster management authorities.

https://news.1rj.ru/str/datasets1

Lightening Strikes Dataset NOAA

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https://news.1rj.ru/str/datasets1

Amazon reviews - Full

Amazon reviews - Full

Abstract:
34,686,770 Amazon reviews from 6,643,669 users on 2,441,053 products, from the Stanford Network Analysis Project (SNAP). This full dataset contains 600,000 training samples and 130,000 testing samples in each class.

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https://news.1rj.ru/str/datasets1

Liver Cancer Predictions

A Global Look at Liver Cancer Cases, Risk Factors, and Healthcare Access

About Dataset
This dataset contains information about liver cancer predictions in the 30 most populated countries. It includes factors such as age, gender, alcohol use, healthcare access, and screening availability. The data helps understand how liver cancer spreads, which groups are at higher risk, and how healthcare differences affect survival chances.

Liver Cancer Predictions

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https://news.1rj.ru/str/datasets1

Oral Cancer Prediction Dataset – Top 30 Countries

📊 160,292 Records | 25 Factors | High-Risk Groups Analyzed

This dataset provides information on oral cancer cases from the 30 most populated countries. It considers key risk factors such as age, gender, tobacco/alcohol use, socioeconomic background, and diagnosis stage. The dataset helps researchers and healthcare experts understand trends in oral cancer and predict outcomes based on patient details.

Oral Cancer Prediction Dataset – Top 30 Countries

#KaggleDatasets #DataScience #MachineLearning #DataAnalysis #DataVisualization #OpenData #DataCleaning #TextClassification #NLP #SentimentAnalysis #BigData #APIAutomation #DataLicensing #SocialMediaData #PythonIntegration #DataModeling #kaggle #ComputerVision #python #LLM #DeepLearning #Pytorch #HuggingFace #Dataset

https://news.1rj.ru/str/datasets1

Thyroid Cancer Risk Dataset

Assessing Thyroid Cancer Risk Through Key Health Indicators

This dataset incorporates 212,691 statistics related to* thyroid cancer risk factors*. It includes demographic facts, clinical history, lifestyle factors, and key thyroid hormone degrees to assess the probability of thyroid most cancers. The dataset may be beneficial for system learning fashions aiming to predict thyroid most cancers risk based on numerous indicators.

Column Denoscriptions:
Patient_ID (int): Unique identifier for each patient.
Age (int): Age of the patient.
Gender (object): Patient’s gender (Male/Female).
Country (object): Country of residence.
Ethnicity (object): Patient’s ethnic background.
Family_History (object): Whether the patient has a family history of thyroid cancer (Yes/No).
Radiation_Exposure (object): History of radiation exposure (Yes/No).
Iodine_Deficiency (object): Presence of iodine deficiency (Yes/No).
Smoking (object): Whether the patient smokes (Yes/No).
Obesity (object): Whether the patient is obese (Yes/No).
Diabetes (object): Whether the patient has diabetes (Yes/No).
TSH_Level (float): Thyroid-Stimulating Hormone level (µIU/mL).
T3_Level (float): Triiodothyronine level (ng/dL).
T4_Level (float): Thyroxine level (µg/dL).
Nodule_Size (float): Size of thyroid nodules (cm).
Thyroid_Cancer_Risk (object): Estimated risk of thyroid cancer (Low/Medium/High).
Diagnosis (object): Final diagnosis (Benign/Malignant).