- Import the battery data into Jupyter Notebook and create a Pandas Data Frame to store the data. (Jupyter notebook can be downloaded using anaconda or directly if you are using Linux.)
- Clean the data to remove any missing values, outliers, or errors.
- Transform the data to calculate the following metrics:
- Average Battery Percentage: the average battery percentage for each day.
- Average Battery Temperature: the average battery temperature for each day.
- Average Voltage: the average voltage for each day.
- Average Current: the average current for each day.
- Daily Battery Usage: the amount of battery used each day.
- Battery Runtime: the total time the battery is used per day. You can add other metrics that can help you explain more the battery behavior.
- Visualize the data by creating charts that provide insights into the battery behavior, such as:
- How does the battery usage change over time?
- How does the battery health change over time?
- How does the battery temperature affect battery usage?
- What is the average daily battery usage, and is it consistent?
numpy
pandas
ydata_profiling
seaborn
matplotlib
sklearn (sci-kit learn)