Global floor area is increasing every year which is subsequently leading to an increase in electricity and heating demand in buildings. Residential buildings have huge potential for energy savings and there is an immediate need to decarbonize them by the end of 2050. Machine learning is finding application across all fields and will thus have an important role to play in the building sector also. One of the important challenges that building owners need to tackle is occupancy detection in residential apartments which can help save considerable amounts of energy and costs. However, occupancy is highly variable, and it is difficult to quantify and predict occupancy because of the random and individualistic nature of humans. In addition, scalable approaches for occupancy detection should prioritize data from common and cost-effective sensors like temperature sensors. In contrast to existing literature which has stated that occupancy detection based on the data from a single environmental sensor is not appropriate for obtaining good results, this paper aims to detect occupancy in a real residential building using only indoor temperature as the feature to train the model. Different machine learning models and techniques are studied and tested to understand how the accuracy of occupancy detection can be increased. With the right techniques, it has been possible to obtain promising results in the form of an accuracy of 95% using machine learning models and only indoor temperature to train it.