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https://github.com/arnabsaha7/piezoelectric-roads-implementation

This repository implements a piezoelectric road system in Python, leveraging Pandas, NumPy, scikit-learn, Matplotlib, and Seaborn. The requirements.txt file ensures version consistency for reproducibility.
https://github.com/arnabsaha7/piezoelectric-roads-implementation

pandas-python piezoelectric roads scikit-learn

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This repository implements a piezoelectric road system in Python, leveraging Pandas, NumPy, scikit-learn, Matplotlib, and Seaborn. The requirements.txt file ensures version consistency for reproducibility.

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README 

        
# Implementation of Piezoelectric Roads



This repository contains the code and analysis for the project on implementing piezoelectric roads. The objective of this project is to analyze the potential of piezoelectric materials in road surfaces for generating electricity through vehicular traffic.



## Table of Contents



- [Introduction](#introduction)

- [Dataset](#dataset)

- [Installation](#installation)

- [Notebook Structure](#notebook-structure)

- [Results](#results)

- [Environmental Impact](#environmental-impact)

- [Future Predictions](#future-predictions)

- [Contributing](#contributing)

- [License](#license)



## Introduction



Piezoelectric materials generate electric charge in response to mechanical stress. This project explores the feasibility of using these materials in road surfaces to harness the energy from vehicles. The analysis includes data loading, statistical analysis, feature selection, model training, and evaluation.



## Dataset



The dataset used in this project includes columns related to energy generation potential, efficiency, durability, and various limitations and considerations. The dataset is a generated synthetic data with high-grade prompting and feature engineering.



```txt

data/generated_data.csv      #----->  main dataset

data/future_data.csv         #----->  small generated data to make further predictions

data/scores.csv              #----->  Performance scores of all models

```



## Installation



To run the notebook, you need to have Python3 and Jupyter Notebook installed. You can install the required libraries using the following commands:



```bash

pip install -r requirements.txt

```



The `requirements.txt` file should include the following libraries:



```txt

numpy

pandas

matplotlib

seaborn

scikit-learn

xgboost

catboost

category_encoders

imblearn

```



## Notebook Structure



The notebook is organized into several sections:



### 0. Import Libraries



In this section, all necessary libraries are imported.



```python

# Basic libraries

import numpy as np

import pandas as pd

import matplotlib.pyplot as plt

import seaborn as sns



# Scikit-learn utilities

from sklearn.feature_selection import SelectKBest, f_classif

from sklearn.model_selection import train_test_split, GridSearchCV

from sklearn.preprocessing import StandardScaler, PolynomialFeatures, LabelEncoder

from sklearn.impute import SimpleImputer

from sklearn.compose import ColumnTransformer

from sklearn.pipeline import Pipeline

from sklearn.utils import shuffle

from imblearn.over_sampling import SMOTE

from sklearn.decomposition import PCA

from sklearn.exceptions import NotFittedError



# Classification models

from sklearn.linear_model import LogisticRegression, SGDClassifier

from sklearn.tree import DecisionTreeClassifier

from sklearn.ensemble import (

    RandomForestClassifier, GradientBoostingClassifier, AdaBoostClassifier, BaggingClassifier

)

from sklearn.svm import SVC

from sklearn.neighbors import KNeighborsClassifier

from sklearn.naive_bayes import GaussianNB

from xgboost import XGBClassifier

from catboost import CatBoostClassifier



# Metrics

from sklearn.metrics import (

    accuracy_score, confusion_matrix, classification_report, precision_score, recall_score, f1_score, roc_curve, auc, roc_auc_score

)



# Encoders

from category_encoders import BinaryEncoder, TargetEncoder, OneHotEncoder



# Additional imports (if required)

import random

import joblib

import warnings

warnings.filterwarnings('ignore')

```



### 1. Data Loading



This section involves loading the dataset and performing initial data exploration.



```python

df = pd.read_csv('data/generated_data.csv')

```



### 2. Statistical Analysis



Here, statistical analysis and visualizations of the dataset are performed to understand the distributions and relationships between different variables.



### 3. Data Preprocessing



Data preprocessing steps such as handling missing values, encoding categorical variables, and scaling are conducted in this section.



### 4. Feature Selection



Feature selection techniques are applied to identify the most important features for the model.



### 5. Model Training and Evaluation



Various classification models are trained and evaluated on the dataset. The models used include:



- Logistic Regression

- Stochastic Gradient Descent (SGD) Classifier

- Decision Tree

- Random Forest

- Gradient Boosting

- AdaBoost

- Bagging Classifier

- Support Vector Machine (SVM)

- K-Nearest Neighbors (KNN)

- Naive Bayes

- XGBoost

- CatBoost



![Training and Evaluation](images/training.png)



### 6. Results



The results of the different models are compared, and the best model is selected. Below is a summary of our findings and future predictions.



- **Best Models**: Decision Tree | XGBoost | Random Forest

- **Accuracy**: 1 | 1 | 0.9733

- **ROC AUC**: 1 | 1 | 0.9560

- **F1 Score**: 1 | 1 | 0.9812



The project demonstrates the potential of using piezoelectric materials in roads for energy generation. The best-performing model is identified based on accuracy and other evaluation metrics.



### Future Predictions



This section highlights the potential future predictions and insights gained from the analysis, focusing on the implementation and scalability of piezoelectric roads.



![Future Predictions](images/results.png)



## Environmental Impact



To understand the broader implications, we analyze the environmental impact of implementing piezoelectric roads across different countries.



### Proportion of Successful Projects by Country



![Successful Projects Proportions](images/country_success.png)



### Environmental Impact by Country



The following chart illustrates the environmental impact measured by average CO2 reduction and energy savings:



![Environmental Impact](images/environmental_impact.png)



## Contributing



Contributions are welcome! Please read the [contributing guidelines](CONTRIBUTING.md) for more details.



## License



This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.