Sensor Data Classification

1. Understanding the Dataset

The dataset consists of time-series sensor data collected over time, where each row represents a sensor reading at a given timestamp. The goal is to classify whether the signal "Passes" or "Fails" based on sensor values.

Dataset Structure

• Time: Timestamp of the recorded signal.
• Feature Columns (0-589): Sensor readings capturing various measurements.
• Pass/Fail: Target variable indicating whether the signal was classified as a pass (1) or fail (-1).

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2. Data Preprocessing

To prepare the dataset for analysis and model training, the following steps are taken:

A. Handling Missing Values

• Check for missing or NaN values in sensor readings.
• Strategy: Replace missing values with mean/median or remove rows if they are too sparse.

B. Feature Scaling

• Sensor readings have different ranges, so Standardization (StandardScaler) or MinMax Scaling is applied.

C. Train-Test Split

• The dataset is split into training (80%) and testing (20%) subsets using train_test_split().

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3. Exploratory Data Analysis (EDA)

A. Visualizing Sensor Distributions

• Histograms: Show how individual sensor readings are distributed.
• Boxplots: Identify outliers in the sensor data.

B. Correlation Analysis

• Heatmap (Seaborn): Identify strongly correlated sensor features.
• Feature Reduction: Remove redundant sensors that provide duplicate information.

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4. Feature Selection & Engineering

• Select the most important sensor readings that impact the Pass/Fail classification.
• Feature engineering techniques like Principal Component Analysis (PCA) or Feature Importance (Random Forest/Gradient Boosting) may be used.

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5. Model Training & Evaluation

Multiple classification models are trained and evaluated:

A. Models Used

• Logistic Regression (Baseline)
• Decision Tree Classifier
• Random Forest Classifier
• Gradient Boosting Classifier
• Naïve Bayes Classifier
• Support Vector Machine (SVM)
• Stacking Classifier (Combining multiple models)

B. Performance Metrics

• Accuracy Score
• Precision, Recall, F1-score
• Confusion Matrix for classification performance.

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6. Insights & Business Impact

• What sensor readings indicate a failure?
  • Certain sensor values may be critical in identifying defective signals.
• How accurate is the failure prediction?
  • If models achieve high accuracy (>85%), they can be deployed in real-world signal monitoring.
• How can this help businesses?
  • Automated detection of failing signals reduces manual inspections and improves production efficiency.

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