Detecting whether a customer will be satisfied or not with the airline
In the aviation industry, which is recuperating quickly after the subset of the Covid-19 pandemic, this will be of great aid to flyers looking to travel. We feel that it will especially be of great use for first time flyers, future flyers/customers to pick out an airline in order for them to travel in comfort and convenience. Output consolidated from the data can help us decide to choose an airline considering the various parameters.
Datasets used:
- Pandas: For data manipulation and analysis
- NumPy: For matrix multiplication, standardization, normalization
- Matplotlib: To create charts using pyplot
- Sklearn: Various sub-libraries efficient tools for predictive data analysis
- Seaborn: Python data visualization library based on matplotlib
- K Neighbours Classifier: Data classification based on proximity to surrounding points
- Logistic regression: used to calculate the probability of a binary (satisfied/unsatisfied) event occurring
- Random Forest: building the best feasible model using ensemble techniques on trees