Here is the English translation of your project description:
Project Context
Airline ticket prices depend on many factors: flight duration, booking date, number of stopovers, airline, etc.
You work for a booking platform and must:
- Understand the factors influencing prices
- Analyze the data
- Build a reliable predictive model
The dataset contains 300,000+ flight bookings.
👉 The goal is to predict the price with controlled error.
🎯 Objectives
- Perform a rigorous exploratory data analysis (EDA)
- Identify relationships between variables and the target
- Formulate and test statistical hypotheses
- Build a reliable preprocessing pipeline
- Train and compare several regression models
- Evaluate a final model
👉 Business objective: MAE < €15 with confidence interval
📊 Project Phases
1️⃣ Data Loading
- Import necessary libraries
- Load the dataset
Clean_Dataset.csv - Check the shape and structure of the data
2️⃣ Technical Variable Analysis
- Objective: understand the nature of each variable
- Identify discrete and continuous variables
- Check data types
- Identify necessary transformations
3️⃣ Univariate Analysis
- Objective: analyze each variable independently
- For each variable:
- Categorical variables: class distribution, visualization (bar plot)
- Numerical variables: descriptive statistics, histogram, and boxplot
4️⃣ Multivariate Analysis
- Objective: understand relationships with price
- Study the impact of:
- Flight duration vs. price
- Airline vs. price
- Number of stopovers vs. price
- Days before flight vs. price
- Use: boxplots, binning, visual analyses
5️⃣ Hypothesis Formulation and Testing
- Objective: statistically validate observations
- Formulate H0 / H1 hypotheses
- Choose the appropriate statistical test:
- Correlation (Pearson)
- ANOVA
- Interpret the p-value (5% significance level)
6️⃣ Preprocessing
- Objective: prepare data for the model
- Handle missing values
- Encode categorical variables
- Transform ordinal variables
- Standardize numerical variables
- Build an sklearn pipeline
7️⃣ Modeling
- Objective: compare multiple models
- Test: Baseline model (DummyRegressor), Linear Regression, Ridge Regression, and Random Forest
- For each model:
- Cross-validation
- MAE / RMSE / R²
- Performance comparison
8️⃣ Final Evaluation
- Objective: select the best model
- Choose the final model
- Train it on the full training data
- Evaluate on the test set
9️⃣ Confidence Interval
- Objective: quantify uncertainty
- Compute absolute errors
- Estimate MAE
- Calculate standard error
- Build a 95% confidence interval
🔟 Model Export
- Objective: save the final model
- Save the complete pipeline (preprocessing + model)
- Save model metadata
- Prepare the model for future use
👥 Pedagogical Modalities
Organization
- Group work
- Mandatory collaboration across all project stages
⏳ Duration
- 5 working days: April 20 → April 24, 2026, before 5:00 PM
🧰 Tools Used
- Python
- Pandas / NumPy (data manipulation and analysis)
- Seaborn / Matplotlib (visualization)
- Scikit-learn (modeling and ML pipeline)
Evaluation Modalities
1️⃣ Oral Presentation (10 minutes)
- Exploratory Data Analysis (EDA)
- Main business and statistical insights
- Final model selected and justification
2️⃣ Practical Demonstration (10–15 minutes)
- Complete preprocessing pipeline
- Comparison of several regression models
- Justification of the final model choice
3️⃣ Technical Questions (5–10 minutes)
- Statistical tests used and their interpretation
- Preprocessing steps (encoding, scaling, etc.)
- Model choices and comparison logic
Deliverables
- 📓 A well-structured and readable Jupyter notebook
- 📊 Relevant and interpreted visualizations
- 💾 A final model exported in
.joblibformat - 🗂️ A clean GitHub repository with README
- 📽️ A PowerPoint or Canva presentation to present the project and results
Performance Criteria
- ✔ Strict adherence to the steps
- ✔ Quality of exploratory analysis
- ✔ Statistical rigor
- ✔ Pipeline with no data leakage
- ✔ Correct model comparison
- ✔ Interpretation of results
- ✔ MAE objective < €15
- ✔ Clean, structured, and reproducible code
- ✔ Good team organization
Let me know if you need any adjustments or further assistance.