See https://pypi.org/project/FastQuantileLayer/#description

The dataset consists of particulate matter concentration and meteorology data, measured in Singapore, Chinatown, and Central business district from March 13, 2018, to March 16, 2018. The data collectors walked from the Outram district - Chinatown to the Central Business District in Singapore. The measurements were carried out using a hand-held air quality sensor ensemble (URBMOBI 3.0).

The dataset contains information from two URBMOBI 3.0 devices and one reference-grade device (Grimm 1.109). The data from the sensors and Grimm are denoted by the subscript, 's1', 's2', and 'gr', respectively.

singapore_all_pm_25.geojson : The observed PM concentration and meteorology, aggregated using a 25 m buffer around the measurement points.

Information on working with geojson file can be found under GeoJSON .

Units:
PM : µg/m³
Scaled_PM_MM : Dimensionless entity scaled using Min-Max-Scaler (https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.MinMaxScaler.html)
Scaled_PM_SS : Dimensionless entity scaled using Standard-Scaler (https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html)
Air temperature: °C
Relative humidity: %

The measurements are part of the "Effects of heavy precipitation events on near-surface climate and particulate matter concentrations in Singapore". It is funded by the support from Humboldt-Universität zu Berlin for seed funding for collaborative projects between National University of Singapore and Humboldt-Universität zu Berlin.

This dataset contains cleaned 2,245 ageing test traces (time vs. MPPT PCE/ maximum power point tracking power conversion efficiency) for perovskite solar cells with various device stacks and architectures in the pickle (.pkl) format.

The dataset can be loaded with the following commands on Python.

import pickle5 as pickle
import pandas as pd 
import numpy as np

with open('20230303_mySeriesDrop.pkl', "rb") as fh:
  mySeriesDrop = pickle.load(fh)

The following command can be used to call a specific row (row 0) within the dataset.

mySeriesDrop[0]

The next steps to use the dataset is using scaling/ normalisation (for instance using sklearn.preprocessing.MaxAbsScaler) and smoothing (for instance using Savitzky-Golay filter).

The code to run the complete analysis, including self-organising map clustering, can be accessed here: https://doi.org/10.5281/zenodo.8181602.

In the Zip, spectral. npy was the average spectral data of red ginseng, mycotoxins and interference impurities, and label. npy was the corresponding label. Spectral data format was [1200,510] and label data format was [1200,1]. The example of data usage (sklearn in Python database was used to establish the classification model) was as follows:

import numpy as np
from sklearn. model_selection import train_test_split
from sklearn. preprocessing import StandardScaler
from sklearn. neighbors import KNeighborsClassifier
from sklearn. metrics import classification_report, accuracy_score

# Load spectral data and labels
x = np.load('.../spectral.npy')[:,1:-1]
y = np.load('.../label.npy')

x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42)

# Data standardization
scaler = StandardScaler()
x_train = scaler.fit_transform(x_train)
x_test = scaler.transform(x_test)

# Train the KNN model
knn_model = KNeighborsClassifier(n_neighbors=5)
knn_model. fit(x_train, y_train)

# Predict
y_pred = knn_model.predict(x_test)

# Print classification reports and accuracy rates
print("Classification Report:")
print(classification_report(y_test, y_pred))
print("Accuracy Score:")
print(accuracy_score(y_test, y_pred))

import re import pandas as pd from sklearn.tree import DecisionTreeClassifier from sklearn.preprocessing import LabelEncoder from google.colab import drive from sklearn.tree import export_text from sklearn.metrics import accuracy_score

  1. Mount Google Drive

drive.mount('/content/drive')

  2. Baca file Excel

file_path = '/content/drive/MyDrive/Colab Notebooks/AI_GACOR_Cleaned.xlsx' data = pd.read_excel(file_path)

  3. Encode kolom 'Hari'

label_encoder_hari =… See the full description on the dataset page: https://huggingface.co/datasets/jellysquish/Dataset-EfficientDrivingTimeDeterminationSystem.