Title of Project: Mental Health Prediction Model Using Machine Learning

Team Members:

1. Rina Kushwaha
2. Varsha Dubey
3. Aman Khan
4. Akash Kumar Verma

Steps for Execution:

[STEP 1] – Problem Definition Define the goal: Predict whether a person is likely to face mental health issues using survey data.

[STEP 2] – Data Collection Obtain the dataset (e.g., from Kaggle – “Mental Health in Tech Survey”).

[STEP 3] – Data Preprocessing

Handle missing values

Encode categorical variables (Label Encoding / One-Hot Encoding)

Scale numerical data (e.g., StandardScaler)

Remove outliers if necessary

[STEP 4] – Exploratory Data Analysis (EDA)

Analyze distributions and relationships between features

Use visualizations (histograms, heatmaps, boxplots)

[STEP 5] – Feature Selection

Choose relevant columns (e.g., age, gender, family history, workplace support)

Optionally use feature importance from models like Random Forest

[STEP 6] – Model Selection & Training

Choose machine learning algorithms (e.g., Logistic Regression, Random Forest, SVM)

Train models using training dataset

Use train_test_split or cross-validation

[STEP 7] – Model Evaluation

Evaluate using metrics: Accuracy, Precision, Recall, F1-score

Use Confusion Matrix to understand results better

[STEP 8] – Model Tuning (Optional)

Tune hyperparameters using Grid Search or Random Search for better performance

[STEP 9] – Model Testing

Test the final model on unseen test data

Ensure it generalizes well and is not overfitting

[STEP 10] – Model Deployment (Optional)

Create a web interface using Streamlit or Flask

Deploy on platforms like Heroku or Render

Let users input values and get predictions

[STEP 11] – Documentation & Report Preparation

Document each step (code, logic, and results)

Prepare project report and presentation for submission/viva

Checklist:

1. Final Project Report
2. Certificate VII Semester (Dated: December 2024).
3. Certificate VIII Semester (Dated: May 2025).
4. Synopsis
5. Final Presentation
6. Source Code
7. Database dump (.sql file)
8. If a web project, then a Docker file for deployment
9. README (This file)