In today's dynamic market, customers have unprecedented access to information, reshaping the traditional buying cycle. Airlines need to be proactive in acquiring customers, leveraging data and predictive models. The task at hand involves manipulating and preparing customer booking data to build a high-quality predictive model.
The primary goal is to predict customer bookings before they embark on their holidays using a predictive model. This involves exploring, manipulating, and preparing the provided dataset to ensure the highest quality for training machine learning algorithms.
- Explore and Prepare the Dataset
- Perform eda to learn more about the data
- Explore potential new features to enhance the predictive model.
- Train a Machine Learning Model
- Select an algorithm suitable for outputting information about variable contributions (e.g., RandomForest).
- Train the machine learning model using the prepared dataset.
- Evaluate Model and Present Findings
- Conduct cross-validation to assess model performance.
- Output relevant evaluation metrics to gauge predictive accuracy.
- Create visualizations to interpret variable contributions.
- Summarize key findings in a single slide for management review.
1.Exploratory Data Analysis (EDA) Report:
- Summary of dataset exploration.
- Identification of new features and rationale.
2.Predictive Model Implementation:
- Utilize an algorithm that provides insights into variable contributions (e.g., RandomForest).
3.Model Evaluation:
- Cross-validation results.
- Key evaluation metrics.
- Hyperparameter tuning.
4.Visualization:
- Visual representation of how each variable contributes to the predictive model.
- Recommendations based on the model's predictive power
