Name :Muneppa Sai kiran
Company :CODTECH IT SOLUTIONS
ID :CT08DS9789
DOMAIN :ARTIFICIAL INTELLIGENCE

Overview of the Project

1. Data Preparation:

• Missing Value Handling: Missing values are imputed for the Age, Salary, and Gender columns using the median (for numeric) and mode (for categorical).
• Outlier Removal: Outliers in the Salary column are removed by filtering values that fall within 2 standard deviations from the mean.
• Duplicates Removal: Duplicate rows in the data are dropped.

2. Feature and Target Setup:

• Features (X): Age, Salary, and City are used as features, with Gender as the target variable.
• Target (y): The Gender column is set as the target for classification.

3. Data Preprocessing:

• Numerical Features Scaling: Age and Salary are standardized using StandardScaler.
• Categorical Features Encoding: City is one-hot encoded using OneHotEncoder.
• A ColumnTransformer is used to apply these transformations to the data.

4. Class Imbalance Handling:

• Class Weight Calculation: Class weights are computed using compute_class_weight('balanced', ...) to account for any class imbalance in the target variable (Gender).

5. Model Setup:

• A RandomForestClassifier is used for classification, with hyperparameters set for n_estimators=100 and max_depth=5.
• The model is part of a Pipeline, where preprocessing and classification steps are integrated.

6. Data Splitting:

• The data is split into training and test sets (80% training, 20% testing) using train_test_split.

7. Model Training and Evaluation:

• The model is trained on the training data and evaluated on the test data.
• Accuracy: The accuracy of the model on the test set is printed.
• Predictions: The model's predictions on the test set are compared to the true labels (Gender).

8. Display Processed Data:

• The code also prints out the transformed training and test datasets after preprocessing to show how the data is processed before feeding it into the model.

Expected Output:

• Class distribution: Shows the distribution of the target variable (Gender), which helps identify any class imbalance.
• Model accuracy: Displays the accuracy of the model on the test data.
• Predictions vs True Labels: Compares predicted and true Gender values to evaluate performance.

Summary of the Workflow:

1. Handle missing values and outliers.
2. Preprocess data (scale numerical features and one-hot encode categorical features).
3. Compute class weights for imbalanced data.
4. Set up a pipeline with preprocessing and classification.
5. Train the model on the training data and evaluate it on the test data.
6. Print out the processed data and model results.

This workflow provides a complete pipeline for classification with preprocessing, model fitting, and evaluation.
THE OUTPUT IS: