Added Data Preprocessing, Outlier Detection, and Visualization #3
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SajanGhimire1 wants to merge 7 commits intoCodeByPinar:mainfrom
Open
Added Data Preprocessing, Outlier Detection, and Visualization #3SajanGhimire1 wants to merge 7 commits intoCodeByPinar:mainfrom
SajanGhimire1 wants to merge 7 commits intoCodeByPinar:mainfrom
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…n techniques in the Plant Health Monitoring project. Integrated Plotly for visualization, replacing some of the existing Matplotlib and Seaborn plots with Plotly equivalents.
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Data Preprocessing:_ Handling missing values and outliers:
Handling Missing Values:
The script applies pd.to_numeric with errors='coerce' to convert non-numeric values to NaN, and then drops columns with missing values using dropna().
Handling Outliers: _Outliers are detected using the Isolation Forest algorithm from scikit-learn.
Model Selection:_Testing various algorithms to identify the best-performing model:
The script only uses the Isolation Forest algorithm for outlier detection. It doesn't involve testing multiple algorithms for model selection.
Data Visualization: Creating insightful visualizations for better understanding:
The script includes various visualizations such as correlation heatmaps, pair plots, histograms, and boxplots, which help in understanding the data and identifying patterns.