This project focuses on the analysis of weather conditions and their potential impact on the performance of Base Transceiver Stations (BTS) in Orange's network and competitors' networks. The analysis encompasses a variety of tasks, including data preparation, statistical analysis, comparative analysis, visualization, and the application of machine learning models.
- Weather data is sourced from files labeled "A..", "B...", etc., providing temperature indicators for stations on different days in 2017.
- Station locations are specified in "kody_stacji.csv," with parameter codes explained in "kody_parametr.csv."
- Information about BTS is available in "bts.csv," detailing the operator, location, and supported technology.
- Anomalies in BTS functionality are recorded in "bts_anomalie_pct.xlsx," indicating the percentage of anomalies each month.
- Merged data from various sources using appropriate identifiers.
- Aggregated weather data to daily averages.
- Combined location data by linking geographic coordinates with place names.
- Conducted exploratory data analysis, summarizing and analyzing datasets.
- Compared Orange's network with competitors' networks based on relevant parameters.
- Visualized weather data and network locations on a map.
- Identified stations prone to extreme weather conditions or experiencing significant weather changes.
- Applied clustering techniques to identify stations with similar weather conditions and anomaly rates.
- Evaluated the impact of weather on BTS anomalies.