JosephineKa/Malawi-Travel-Cost-Predictor-
Type de record:
model
Créateur:
Jos
HĂ´te:
Machine learning model approach to predict trip costs to Malawi using Polynomial Regression
# 🇲🇼 Malawi Travel Cost Predictor
> A machine learning system that predicts total trip costs for travelers to Malawi with near-perfect accuracy (R² = 1.000)
## đź“‹ Overview
Planning a trip to Malawi involves navigating complex and variable costs, including flights, accommodation, and tour packages. This project develops a **machine learning-based predictive tool** that analyzes historical travel data to accurately estimate total trip expenses.
Using a synthetically generated dataset of 100 tour records derived from official Malawi tourism pricing sources, we implemented and evaluated three regression models:
- **Linear Regression** (Baseline)
- **Lasso Regression** (Feature selection)
- **Polynomial Regression Degree 2** (Best performer)
### Key Results
| Model | R² Score | RMSE | MAE |
|-------|----------|------|-----|
| Linear Regression | 0.48 | $860.13 | $2380.59 |
| Lasso Regression | 0.94 | $262.30 | $12.73 |
| **Polynomial Regression** | **1.00** | **~$0** | **~$0** |
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## 🎯 Problem Statement
Travelers and tourism professionals face difficulty predicting total expenses due to:
- Multiple interacting cost factors (flights, hotels, tours)
- Seasonal price variations
- Complex package deal interactions
- Non-linear pricing structures
**Our Solution:** An ML-powered prediction tool that captures non-linear relationships to provide accurate cost estimates.
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## đź“Š Dataset
### Data Sources
Due to limited real-world data, a synthetic dataset was generated from three official tourism sources:
| Source File | Records | Description |
|-------------|---------|-------------|
| `airlinesdates11.xlsx` | 60 | Airline routes, flight times, and costs |
| `allregions.xlsx` | 25 | Accommodation rates and details |
| `specificplace1.csv` | 22 | Tour package information |
### Generated Dataset
- **100 synthetic tour records** created by combining random samples from each source
- **10 categorical features** ( …
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