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Data Preprocessing

  1. Added data preprocessing pipeline (encoding, scaling, train-test split)
  2. Handled missing values and outliers for better model performance
  3. Engineered new features (Efficiency_Per_Hour, Meeting_Impact)

Exploratory Data Analysis (EDA)

  1. Plotted correlation matrix & pairplots for insights
  2. Visualized productivity distribution and key features
  3. Analyzed impact of meetings & work-life balance on productivity

Machine Learning Models

  1. Implemented Random Forest Classifier for productivity categories
  2. Trained regression model to predict Productivity_Score
  3. Tuned hyperparameters for better model accuracy

Evaluation & Optimization

  1. Generated classification report & accuracy metrics
  2. Computed R², MAE, RMSE for regression performance
  3. Optimized feature selection for improved model performance

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