Airline-Price-Predict

To perform a comprehensive analysis of the flight booking dataset, incorporating exploratory data analysis (EDA), statistical methods, and machine learning algorithms, we can follow these steps:

1. Data Understanding and Cleaning:

   • Load the dataset and understand its structure, features, and data types.
   • Handle missing values, outliers, and any inconsistencies in the data.
   • Convert categorical variables into numerical representations if necessary.

2. Exploratory Data Analysis (EDA):

   • Explore the distribution of key variables such as flight prices, departure times, arrival times, airline carriers, etc.
   • Visualize relationships between variables using scatter plots, histograms, box plots, etc.
   • Analyze trends, seasonality, and patterns in flight bookings over time.
   • Investigate correlations between features to identify potential insights.

3. Statistical Analysis:

   • Perform statistical tests to validate hypotheses or uncover significant differences between groups (e.g., comparing prices between different airlines or routes).
   • Calculate descriptive statistics to summarize key characteristics of the dataset.

4. Feature Engineering:

   • Create new features that might be predictive of flight prices or booking patterns, such as day of the week, time of day, etc.
   • Encode categorical variables using techniques like one-hot encoding or label encoding.

5. Machine Learning Modeling:

   • Split the dataset into training and testing sets.
   • Select appropriate machine learning algorithms based on the nature of the problem (e.g., regression for price prediction, classification for predicting flight delays).
   • Train models using the training data and evaluate their performance using suitable metrics (e.g., Mean Absolute Error for regression, accuracy for classification).
   • Tune hyperparameters to optimize model performance.
   • Validate models using cross-validation techniques to ensure generalizability.

6. Interpretation and Visualization:

   • Interpret the results of machine learning models and statistical analyses in the context of the problem domain.
   • Visualize model predictions, feature importance, and other relevant insights to communicate findings effectively.
   • Provide actionable recommendations based on the analysis to improve the flight booking experience for passengers.

By following these steps, we can conduct a comprehensive analysis of the flight booking dataset, leveraging EDA, statistical methods, and machine learning algorithms to extract meaningful information and insights that can benefit passengers and stakeholders in the aviation industry.