Room Occupancy detection data set - classification

This dataset provides information about the room's environmental factors such as temperature, humidity, light, Co2 Humidity ratio and occupancy. We can use this dataset for predicting occupancy in an office room. There are three dataset available, one for training and two for testing the models considering the office door opened and closed during occupancy. The target variable occupancy 0 and 1,

Algorithms to be used , Random forest, svm’s, GaussianNB classifier, Decision tree Classifier, Logistic regression etc

Dataset for Predicting room occupancy using environmental factors.

Data set Link : UCI MLR - https://archive.ics.uci.edu/ml/datasets/Occupancy+Detection+

Kaggle : - https://www.kaggle.com/robmarkcole/occupancy-detection-data-set-uci

Detailed overview of dataset

Data is provided with date-time information and six environmental measures taken each minute over multiple days, specifically

Occupancy, 0 or 1, 0 for not occupied, 1 for occupied status

Dataset

import pandas as pd
 #Load data
 
 file_loc = "data\\datatraining.txt"
 occupancy_data = pd.read_csv(file_loc)
 occupancy_data.head()

Total number of rows and column in the dataset

r_c=occupancy_data.shape
 print("Total number of record in the dataset : ",r_c[0])
 print("Total number of columns in the dataset : ",r_c[1])

Check Details

# Data information
 occupancy_data.info()

Check the number of missing values in the dataset

# check missing values in each column
 occupancy_data.isna().sum()
 

Statistical information

# statistical information
 occupancy_data.describe()

Data Visulization

Correlation

import seaborn as sns
 import matplotlib.pyplot as plt
 # correlation
 corr = occupancy_data.corr()
 corr.style.background_gradient(cmap='coolwarm')

Box plot

num_cols = occupancy_data.select_dtypes(exclude ='object').columns
 for col in num_cols:
  plt.boxplot(occupancy_data[col])
  plt.xlabel(col)
  plt.show()
 

occupancy_data.set_index('date', inplace=True)
 
 for i in range(len(num_cols)):
  occupancy_data.iloc[:,[i]].plot(figsize=(15,5))
  plt.xticks(rotation=45,size=10)
  plt.yticks(size=10)

Tempreture

Humidity

Light

Co2

Humidity Ratio

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