JayGajera/TimeSeriesSmellSensorDataForClassification

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Project Title: Anonymized Smell Classification

Project Overview

This project focuses on the analysis and classification of smell data collected from four different devices: Device A, Device B, Device C, and Device D. These devices are strategically placed in different locations and operate continuously, recording measurements approximately every 1.2 seconds. The primary objective of this project is to classify various target smells while preserving the anonymity of the specific smells.

The data is stored in .parquet format and contains multiple features, including a target class y that ranges from 0 to 52. The class 0 represents the background environment, while classes 1-52 represent various target smells. Due to confidentiality, the exact nature of these smells is anonymized.

Data Structure

Each device stores data with the following columns:

• timestamp: The time at which the measurement was recorded.
• f_89, f_85, f_47, f_94, f_33, f_59, f_41, f_43, f_90, f_61, f_53, f_42, f_67, f_40, f_69: Sensor readings from various sensors on the device.
• file_name: The name of the file from which the data is extracted.
• temperature: Ambient temperature recorded by the device.
• humidity: Ambient humidity recorded by the device.
• y: The class label representing either background (0) or one of the 52 target smells.

Device Details

• Device A, B, and C: These devices have been operational for a longer period and have been collecting data continuously. However, there may be gaps in the data collection due to operational downtimes, which could last for days or weeks.

• Device D: This device was introduced later and therefore has a shorter data collection period compared to the other three devices.

Anonymized Labels

To protect the confidentiality of the target smells, the class labels have been anonymized as follows:

Anonymized Labels:
0: Label 1
1: Label 2
2: Label 3
3: Label 4
4: Label 5
5: Label 6 (similar to Label 5)
6: Label 7
7: Label 8
8: Label 9
9: Label 10
10: Label 11 (similar to Label 7)
11: Label 12 (similar to Label 5)
12: Label 13
13: Label 14
14: Label 15
15: Label 16
16: Label 17
17: Label 18
18: Label 19
19: Label 20
20: Label 21
21: Label 22
22: Label 23
23: Label 24
24: Label 25
25: Label 26
26: Label 27
27: Label 28
28: Label 29
29: Label 30 (similar to Label 24)
30: Label 31
31: Label 32
32: Label 33
33: Label 34
34: Label 35 (similar to Label 25)
35: Label 36
36: Label 37
37: Label 38
38: Label 39
39: Label 40
40: Label 41
41: Label 42
42: Label 43
43: Label 44
44: Label 45 (similar to Label 44)
45: Label 46
46: Label 47
47: Label 48 (similar to Label 47)
48: Label 49
49: Label 50
50: Label 51
51: Label 52
52: Label 53

Some labels are noted as being similar to others, indicating potential overlap or similarity in the type of smell detected. These relationships are important for further analysis.

Methodology

Data Storage and Retrieval

The data collected from the devices is stored in Parquet format, which is a highly efficient columnar storage format. To reduce the file size and improve I/O performance, the Parquet files are saved using Snappy compression.

Saving the Data

The DataFrame is saved to a Parquet file using the following command:

# Save the DataFrame to a Parquet file with Snappy compression
df_deviceD.to_parquet("device_D.parquet", index=False, compression='snappy')

Reading the Data

To load the Parquet file back into a pandas DataFrame, you can use the following command:

# Read the Parquet file into a DataFrame
df_loaded = pd.read_parquet("device_D.parquet")

The use of Snappy compression is automatically handled during the read operation, so you do not need to specify the compression method when reading the file.

Data Preparation

1. Loading Data: The .parquet files from each device will be loaded into a pandas DataFrame for analysis.
2. Handling Missing Data: Given the possibility of missing data (days/weeks), appropriate imputation or filtering techniques should be applied.
3. Anonymizing Labels: As shown, the y class labels have been anonymized to preserve confidentiality.

Analysis and Modeling

This dataset presents a unique opportunity to explore and implement advanced AI and machine learning techniques. We are particularly interested in leveraging the expertise of your team to push the boundaries of what can be achieved with this data. Below are some areas of exploration and methodologies we encourage you to consider:

1. Exploratory Data Analysis (EDA):

   • Comprehensive Data Profiling: Conduct a thorough examination of the dataset, identifying key patterns, trends, and anomalies. We are interested in understanding how the data varies across different devices and environmental conditions.
   • Multivariate Analysis: Explore interactions between various sensors, temperature, and humidity to uncover any latent relationships that might influence classification performance.
   • Temporal Dynamics: Analyze the data over time, particularly focusing on how sensor readings and labels evolve. This could involve time-series analysis, identifying temporal dependencies, and assessing the impact of missing data.

2. Advanced Modeling Techniques:

   • Deep Learning Architectures: Consider implementing advanced neural network architectures such as Convolutional Neural Networks (CNNs) for feature extraction, Recurrent Neural Networks (RNNs) or Long Short-Term Memory networks (LSTMs) for handling temporal dependencies, and Transformer models for capturing complex relationships in the data.
   • Ensemble Methods: Explore ensemble learning techniques such as stacking, boosting (e.g., XGBoost, LightGBM), and bagging to combine the strengths of multiple models and improve classification accuracy.
   • Transfer Learning: Investigate the potential for transfer learning, particularly if there is pre-existing knowledge or models from similar datasets that could be leveraged to enhance performance on this dataset.
   • Semi-Supervised and Unsupervised Learning: Given the possible challenges with imbalanced classes or underrepresented labels, we are interested in approaches that make use of unlabeled data or semi-supervised techniques to improve model robustness.
   • Dimensionality Reduction: Apply advanced dimensionality reduction techniques such as t-SNE, PCA, or UMAP to visualize the data and possibly improve model performance by focusing on the most informative features.

3. Interpretability and Explainability:

   • Model Interpretability: We value transparency in AI, so techniques like SHAP (SHapley Additive exPlanations) or LIME (Local Interpretable Model-agnostic Explanations) should be used to explain model predictions. Understanding which features most influence the predictions will be critical.
   • Error Analysis: Conduct a detailed error analysis to understand where models may be failing, particularly in distinguishing between similar smells or in the presence of background noise.

4. Result Sharing and Reporting:

   • Methodology Documentation: We request that all approaches, including any feature engineering, model selection, and optimization strategies, be thoroughly documented. This should include justifications for the techniques used and any challenges encountered.
   • Performance Metrics: In addition to standard metrics like accuracy, precision, recall, and F1-score, we are interested in more nuanced evaluations, such as confusion matrices, ROC curves, and performance across different devices or time periods.
   • Comparison of Techniques: Please provide a comparative analysis of the different models and approaches used, highlighting the strengths and weaknesses of each. We are particularly interested in understanding how advanced techniques perform relative to more traditional methods.
   • Recommendations for Future Work: Based on your analysis, we would appreciate recommendations on further improvements, potential new features, or alternative approaches that could be explored in future iterations.

Expected Deliverables

• Codebase: Provide the full codebase used for the analysis and modeling, with detailed comments and explanations.
• Detailed Report: A comprehensive report summarizing the methodologies, results, and interpretations. The report should include all relevant visualizations, tables, and figures.
• Presentation: A presentation summarizing the key findings, including the most successful techniques and any significant challenges or limitations encountered.

Documentation and Reporting

• Analysis Report: A comprehensive report detailing the methodology, analysis, and results will be provided.
• Code: All code used in the data analysis and modeling will be documented and shared in the project repository.

Usage Instructions

Setting Up the Environment

1. Install Dependencies:

   • Ensure that Python and pip are installed.
   • Install required libraries using the following command:

     pip install -r requirements.txt
     

2. Running the Code:

   • Load the data using the provided scripts.
   • Execute the analysis and modeling scripts as outlined in the documentation.

Expected Outputs

1. Classification Results: The output will include predictions for the target smells along with performance metrics.
2. Visualizations: Graphs and plots to illustrate the data distribution and model performance.
3. Analysis Report: A detailed report summarizing the findings.

Contributions

Contributions to this project are welcome. If you find any issues or have suggestions for improvements, please feel free to submit a pull request or open an issue.

License

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

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