https://github.com/hugo-hattori/watercraft_values_ai_prediction

Data Science Project.
https://github.com/hugo-hattori/watercraft_values_ai_prediction

ai-model artificial-intelligence artificial-intelligence-algorithms data-analysis data-analytics data-science jupyter jupyter-notebook machine-learning machine-learning-algorithms matplotlib matplotlib-pyplot pandas pandas-dataframe pandas-python python seaborn sklearn sklearn-library sklearn-metrics

Last synced: 6 days ago
JSON representation

Data Science Project.

• Host: GitHub
• URL: https://github.com/hugo-hattori/watercraft_values_ai_prediction
• Owner: Hugo-Hattori
• License: mit
• Created: 2023-07-04T19:38:02.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2023-11-06T21:59:19.000Z (about 1 year ago)
• Last Synced: 2024-11-07T14:32:05.559Z (about 2 months ago)
• Topics: ai-model, artificial-intelligence, artificial-intelligence-algorithms, data-analysis, data-analytics, data-science, jupyter, jupyter-notebook, machine-learning, machine-learning-algorithms, matplotlib, matplotlib-pyplot, pandas, pandas-dataframe, pandas-python, python, seaborn, sklearn, sklearn-library, sklearn-metrics
• Language: Jupyter Notebook
• Homepage:
• Size: 239 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

        
# Watercraft Values AI Prediction

 
This project's goal is to create an AI Model capable of predicting 

watercraft prices based on their composition material, width, length,

watercraft type (e.g. yatch, fishing boat), sell type (e.g. new boat, used boat),

and manufacture year using a database for the model training.


### Packages used:

+ sklearn

+ pandas

+ seaborn

+ matplotlib

## Importing the Database

First we need to import the database, which in this scenario is a .csv file

named "barcos_ref.csv". By using the DataFrame.info() method we can observe

that no data processing will be necessary in this case.


https://github.com/Hugo-Hattori/Watercraft_Values_AI_Prediction/blob/d26e1c7b50cf16faadfc915b3992396f913527f6/Watercraft_Values_AI_Prediction.py#L10-L15

## Exploratory Data Analysis (EDA)

Now we will analyze the correlation between the price and the other 

parameters such as length and composition material.


https://github.com/Hugo-Hattori/Watercraft_Values_AI_Prediction/blob/d26e1c7b50cf16faadfc915b3992396f913527f6/Watercraft_Values_AI_Prediction.py#L17-L21

![img.png](img.png)

## AI Modeling

In this scenario we will be using Linear Regression and Random Forest

Regressor as Machine Learning Algorithms, thus two models will be generated.


For the AI training process it is necessary to split the database

into x_treino, x_teste, y_treino, y_teste where "x" are the parameters

used to achieve the "y" which is the price. Both x_treino and y_treino

are going to be used for the AI training afterwards x_teste and y_teste

will both be used to test the AI Model's accuracy based on the R² Score.


https://github.com/Hugo-Hattori/Watercraft_Values_AI_Prediction/blob/d26e1c7b50cf16faadfc915b3992396f913527f6/Watercraft_Values_AI_Prediction.py#L23-L38

## Graphical Analysis

To make it more evident we will plot graphs that compare both the

Linear Regression and Random Forest Regressor models to "y_teste".


+ Linear Regression X y_teste

![img_1.png](img_1.png)

+ Random Forest Regressor X y_teste

![img_2.png](img_2.png)

## AI Prediction

The model with the highest score is the chosen one to predict

watercraft prices using a different database called "novos_barcos.csv"


https://github.com/Hugo-Hattori/Watercraft_Values_AI_Prediction/blob/d26e1c7b50cf16faadfc915b3992396f913527f6/Watercraft_Values_AI_Prediction.py#L54-L58

The "nova_previsao" variable contains the respective price values

predicted by the AI Model.


 Note: this is a project developed for academic purposes, therefore the

data contained in "barcos_ref.csv" and "novos_barcos.csv" are fictitious 

and used only to learn AI model training applications.