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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.

1. Introduction
2. Features
3. Technologies Used
4. Installation
5. Usage
6. Data Preprocessing
7. Model Training
8. Model Evaluation
9. Contributing
10. License
11. Contact

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.

• 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.

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.

To get started, clone the repository and install the required libraries. Use the following commands:

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.

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:

jupyter notebook

Then navigate to Sensor_Data_Analytics.ipynb and execute the cells to follow along with the analysis.

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.

Here’s a snippet showing how to load and preprocess the data:

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)

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

Here’s a snippet for training a Random Forest model:

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)

Evaluating your model is essential to understand its performance. The notebook includes:

• Confusion Matrix
• ROC Curve
• Classification Report

Here’s how to evaluate your model:

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))

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.

This project is licensed under the MIT License. See the LICENSE file for details.

For any questions or feedback, feel free to reach out:

• Email: ayan007jbond@example.com
• GitHub: Ayan007JBond

Don't forget to check the Releases section for the latest updates and downloadable files.

Thank you for visiting the Sensor Data Analytics repository! Happy coding! 🎉