c_outlier_handling.md

title
Outlier_Handling

Outlier Detection and Handling

Ensuring data quality is paramount for building reliable and accurate machine learning models. This section outlines the approach taken to identify and manage outliers within the Project Apero.

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1. Overview

For detailed information about the data and features, please refer to the Data section.

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2. Importance of Outlier Handling

Outliers can significantly skew the results of data analysis and model training. They may represent anomalies, errors in data collection, or genuine rare occurrences. Proper handling ensures that models are not adversely affected, leading to more robust and generalizable predictions.

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3. Identification of Outliers

Methodology: Z-Score Analysis

We employed the Z-score method to detect outliers in the numerical features of the dataset. The Z-score measures the number of standard deviations a data point is from the mean. Typically, a Z-score threshold of ±3 is used to identify significant outliers.

Steps Involved:

1. Calculation of Z-Scores:

   • For each numerical feature (e.g., age, BMI, glucose levels), compute the Z-score for every data point.

2. Thresholding:

   • Data points with Z-scores beyond ±3 are flagged as potential outliers.

3. Visualization:

   • Box plots and scatter plots are generated to visually assess the distribution of data and the presence of outliers.

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4. Handling Detected Outliers

Chosen Approach: Removal

We opted to remove outliers identified through the Z-score method.

• Impact on Models: Outliers were found to disproportionately influence model parameters, leading to biased predictions.
• Data Integrity: The dataset's integrity was better maintained by removing anomalous data points that did not represent the underlying population.
• Implementation:

  from scipy import stats
  import numpy as np
  
  # Assuming 'data' is the preprocessed DataFrame
  z_scores = np.abs(stats.zscore(data.select_dtypes(include=[np.number])))
  threshold = 3
  data_clean = data[(z_scores < threshold).all(axis=1)]

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5. Impact on Model Training

Removing outliers led to a more balanced and representative dataset, which improved the performance and reliability of our machine learning models.

• Enhanced Accuracy: Models trained on cleaned data showed higher accuracy and better generalization.
• Reduced Variance: The variance in model predictions decreased, indicating more consistent performance.
• Improved Convergence: Training algorithms converged faster without the noise introduced by outliers.

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Conclusion

Effective outlier management has been integral to the success of the Project Apero, ensuring that the models built are both accurate and reliable. By systematically identifying and removing anomalous data points, we have enhanced the quality of our data analysis and the performance of our recommendation system.

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