Analysis and modeling of consumption behavior of tourists in Tanzania
# Tanzania Tourism Prediction: ML Project
The main goal of this project was about to create a ML model to predict how much money a tourist will spend when visiting Tanzania.
The data used for this is: data.
Hypothesis: Tourists are likely to spent more money depending on their age.
To impove our hypothesis we trained a regression model to predict the total cost a tourist can spend.
Contribution features are:
- In what age group is a tourist
- The country a tourist coming from
- Which packages a tourist have
- Number of tourists
## Exploratory Data Analysis (EDA)
## Data Cleaning and Feature Engineering
## Model Building and Evaluation
# Requirements and Environment
Requirements:
- pyenv with Python: 3.9.8
Requirements:
- pyenv with Python: 3.9.8
Environment:
For installing the virtual environment you can either use the Makefile and run `make setup` or install it manually with the following commands:
```Bash
pyenv local 3.9.8
python -m venv .venv
source .venv/bin/activate
pip install --upgrade pip
pip install -r requirements.txt
```
## Usage
In order to train the model and store test data in the data folder and the model in models run:
```bash
#activate env
source .venv/bin/activate
python example_files/train.py
```
In order to test that predict works on a test set you created run:
```bash
python example_files/predict.py models/linear_regression_model.sav data/X_test.csv data/y_test.csv
```
## Limitations
Development libraries are part of the production environment, normally these would be separate as the production code should be as slim as possible.