HERE IS A SUMMARY OF THE PCOS PREDICTION PROJECT I WORKED ON WHICH HAS TO DO WITH DATA PROCESSING AND MODEL TRAINING:

1. Data Import and Inspection:

   • Imported the dataset and checked basic information such as column names, data types, and missing values.

2. Handling Missing Data:

   • Identified and handled missing values for key columns by filling NaNs with appropriate methods.

3. Encoding Categorical Variables:

   • Applied One-Hot Encoding to convert categorical variables into binary columns.
   • Used custom mappings to convert string values in BMI and Stress Levels columns into numeric values.
   • Applied the mappings to the respective columns (BMI & Stress Levels).

4. Data Type Verification:

   • Verified and corrected data types for consistency and ensured that all columns had the correct type.

5. Label Encoding:

   • Initialized and applied LabelEncoder to convert binary/categorical columns to numeric values.

6. Feature Standardization:

   • Standardized numerical features to ensure they are on a comparable scale.

7. Feature and Target Variable Definition:

   • Defined features (independent variables) and target variable (dependent variable).

8. Data Splitting:

   • Split the data into training and testing sets for model evaluation.

9. Model Training and Evaluation:

   • Trained a prediction model on the training set.
   • Evaluated model performance with classification reports and confusion matrix.

10. Model Visualization:

    • Visualized results by plotting feature importance and showing the confusion matrix.

11. Exploratory Data Analysis (EDA):

    • Visualized the distribution of Age and BMI.
    • Used Pairplot to explore relationships between numerical features (Age, BMI, Lifestyle Score, and Stress Levels).
    • Generated a correlation heatmap to analyze feature correlations.