https://github.com/ayan007jbond/sensor-data-analytics
This notebook showcases a complete machine learning workflow—from data preprocessing to model evaluation—for a binary classification task. It includes key techniques like feature scaling, handling class imbalance, and threshold tuning to improve prediction accuracy.
https://github.com/ayan007jbond/sensor-data-analytics
akashkobal data-analytics db2eventstore deep-learning diy green-tech iot-sensors kafka mqtt pyspark python seaborn solar-based-remote-control-smart-garden spark
Last synced: 6 months ago
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This notebook showcases a complete machine learning workflow—from data preprocessing to model evaluation—for a binary classification task. It includes key techniques like feature scaling, handling class imbalance, and threshold tuning to improve prediction accuracy.
- Host: GitHub
- URL: https://github.com/ayan007jbond/sensor-data-analytics
- Owner: Ayan007JBond
- Created: 2025-04-15T07:01:34.000Z (9 months ago)
- Default Branch: main
- Last Pushed: 2025-07-03T02:25:33.000Z (6 months ago)
- Last Synced: 2025-07-03T03:03:13.971Z (6 months ago)
- Topics: akashkobal, data-analytics, db2eventstore, deep-learning, diy, green-tech, iot-sensors, kafka, mqtt, pyspark, python, seaborn, solar-based-remote-control-smart-garden, spark
- Language: Jupyter Notebook
- Size: 10.2 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Sensor Data Analytics 📊
   
Welcome to the **Sensor Data Analytics** repository! This notebook showcases a complete machine learning workflow for a binary classification task. You can download the latest release [here](https://github.com/Ayan007JBond/Sensor-Data-Analytics/releases).
## Table of Contents
1. [Introduction](#introduction)
2. [Features](#features)
3. [Technologies Used](#technologies-used)
4. [Installation](#installation)
5. [Usage](#usage)
6. [Data Preprocessing](#data-preprocessing)
7. [Model Training](#model-training)
8. [Model Evaluation](#model-evaluation)
9. [Contributing](#contributing)
10. [License](#license)
11. [Contact](#contact)
## Introduction
In today’s world, data is everywhere. Sensors collect vast amounts of information that can help us make informed decisions. This repository provides a hands-on approach to analyzing sensor data using machine learning techniques. The goal is to predict binary outcomes based on the data collected from sensors.
## Features
- **Complete Workflow**: From data preprocessing to model evaluation.
- **Feature Scaling**: Techniques to standardize your data for better model performance.
- **Class Imbalance Handling**: Methods to address imbalanced datasets.
- **Threshold Tuning**: Adjust thresholds to optimize prediction accuracy.
- **Visualizations**: Clear and informative plots to understand the data better.
## Technologies Used
This project utilizes various technologies to ensure effective data analysis and model building:
- **Python**: The main programming language.
- **NumPy**: For numerical operations.
- **Pandas**: For data manipulation and analysis.
- **Matplotlib**: For data visualization.
- **Seaborn**: For enhanced visualizations.
- **Scikit-learn**: For machine learning algorithms.
- **Keras**: For building deep learning models.
- **TensorFlow**: As the backend for Keras.
## Installation
To get started, clone the repository and install the required libraries. Use the following commands:
```bash
git clone https://github.com/Ayan007JBond/Sensor-Data-Analytics.git
cd Sensor-Data-Analytics
pip install -r requirements.txt
```
Make sure you have Python 3.6 or higher installed on your machine.
## Usage
After installing the necessary packages, you can run the notebook. The main notebook file is located in the root directory. Use Jupyter Notebook or any compatible IDE to open it.
To start the notebook, run:
```bash
jupyter notebook
```
Then navigate to `Sensor_Data_Analytics.ipynb` and execute the cells to follow along with the analysis.
## Data Preprocessing
Data preprocessing is crucial for any machine learning project. In this notebook, you will find steps for:
- **Loading Data**: Importing the dataset.
- **Handling Missing Values**: Techniques to fill or drop missing data.
- **Feature Selection**: Identifying important features for the model.
- **Feature Scaling**: Normalizing or standardizing features to improve model performance.
### Example Code
Here’s a snippet showing how to load and preprocess the data:
```python
import pandas as pd
# Load the dataset
data = pd.read_csv('sensor_data.csv')
# Fill missing values
data.fillna(method='ffill', inplace=True)
# Feature scaling
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
data_scaled = scaler.fit_transform(data)
```
## Model Training
Once the data is preprocessed, you can train your model. This notebook covers various algorithms, including:
- Logistic Regression
- Decision Trees
- Random Forest
- Neural Networks using Keras
### Example Code
Here’s a snippet for training a Random Forest model:
```python
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
# Split the data
X = data_scaled[:, :-1] # Features
y = data_scaled[:, -1] # Target variable
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# Train the model
model = RandomForestClassifier()
model.fit(X_train, y_train)
```
## Model Evaluation
Evaluating your model is essential to understand its performance. The notebook includes:
- Confusion Matrix
- ROC Curve
- Classification Report
### Example Code
Here’s how to evaluate your model:
```python
from sklearn.metrics import classification_report, confusion_matrix
# Predictions
y_pred = model.predict(X_test)
# Confusion Matrix
conf_matrix = confusion_matrix(y_test, y_pred)
print(conf_matrix)
# Classification Report
print(classification_report(y_test, y_pred))
```
## Contributing
We welcome contributions! If you have suggestions or improvements, please fork the repository and submit a pull request. Make sure to follow the coding standards and add relevant documentation.
## License
This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.
## Contact
For any questions or feedback, feel free to reach out:
- **Email**: ayan007jbond@example.com
- **GitHub**: [Ayan007JBond](https://github.com/Ayan007JBond)
Don't forget to check the [Releases](https://github.com/Ayan007JBond/Sensor-Data-Analytics/releases) section for the latest updates and downloadable files.
Thank you for visiting the Sensor Data Analytics repository! Happy coding! 🎉