Flight Fare Prediction

The dataset used for analysis contains information about past flight routes, departure and arrival cities, airline carriers, date of travel, and corresponding ticket prices. The analysis begins with data preprocessing, including handling missing values, feature engineering, and data normalization.

The project aims to address specific research questions, such as:

1. How do flight ticket prices vary with different factors, such as travel dates, airline carriers, and routes?
2. Are there any seasonal trends or patterns in flight fares?
3. Which features have the most significant impact on ticket prices?

Data visualization techniques, such as line plots, scatter plots, and heatmaps, are employed to visualize trends and relationships between different variables. Descriptive statistics, such as mean, median, and standard deviation, are used to summarize the data.

The project involves training various machine learning models, including regression algorithms like Linear Regression.