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# HR Role Skills/Department Minimum Salary (INR) Maximum Salary (INR) 1 HR Director / Head of HR Strategic HR Management, Leadership, Organizational Development ₹12,00,000 ₹22,00,000 2 HR Manager / Senior HR Manager Employee Relations, Recruitment, HR Strategy, Policy Implementation ₹8,00,000 ₹15,00,000 3 Talent Acquisition Manager / Senior Recruiter Recruitment Strategy, Talent Sourcing, Interviewing, Negotiation ₹7,00,000 ₹14,00,000 4 Compensation and Benefits Manager Compensation Analysis, Benefits Administration, Payroll Management ₹7,00,000 ₹12,00,000 5 Organizational Development (OD) Manager / Specialist Organizational Change, Training & Development, Performance Management ₹6,00,000 ₹11,00,000

    ### **1. Healthcare**   #### **Logistic Regression** - ** Scenario **: Predicting patient survival, disease classification. - ** Tools **: Python (Scikit-learn), R - ** Workflow **:    1. **Data Collection**: Gather patient data (e.g., age, symptoms, vitals).    2. **Preprocessing**: Handle missing data, normalize variables.    3. **Train Model**: Use logistic regression to predict binary outcomes.    4. **Evaluate**: Accuracy, precision, recall, ROC curve.    5. **Deploy**: Use the model for real-time patient survival prediction.   #### **Decision Trees** - ** Scenario **: Patient diagnosis, treatment recommendation. - ** Tools **: Python (Scikit-learn), R, SAS - ** Workflow **:    1. **Data Collection**: Collect patient data (symptoms, test results).    2. **Preprocessing**: Handle missing values, encode categorical features.    3. **Model Train...

### 1. **Linear Regression**    - ** Industry **: Finance, Retail    - ** Scenario **: Predicting continuous outcomes such as sales, stock prices, and trends.    - ** Tools **: Python (Scikit-learn, Statsmodels), R, Excel    - ** Workflow **:      1. Data collection (sales, prices, etc.).      2. Preprocessing: Handle missing values, remove outliers.      3. Feature selection: Identify key factors influencing the outcome.      4. Train and validate the linear regression model.      5. Interpret results and make predictions. ### 2. **Logistic Regression**    - ** Industry **: Healthcare, Marketing    - ** Scenario **: Binary classification problems such as patient survival, email spam detection.    - ** Tools **: Python (Scikit-learn), R, SAS    - ** Workflow **:      1. Data collection (patient records, email text, etc....

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