--> The "Mental Fitness Tracker" project aimed to develop an AI-powered system to monitor and support mental well-being using regression models. The project leveraged a dataset consisting of various mental health indicators to train and evaluate two regression models: Linear Regression and Random Forest Regressor.

--> Initially, the project involved data preprocessing tasks such as loading the dataset using Pandas, handling missing values, and performing label encoding on categorical variables. Exploratory data analysis was conducted using visualizations such as heatmaps, pair plots, and line plots to gain insights into the relationships between different variables and mental fitness.

--> The dataset was split into training and testing sets using the train_test_split function from the sklearn.model_selection module. Both the Linear Regression and Random Forest Regressor models were trained on the training set. The performance of each model was evaluated using metrics such as mean squared error (MSE), root mean squared error (RMSE), and R-squared score.

--> The evaluation results showed that the Random Forest Regressor model outperformed the Linear Regression model, achieving a higher score of 0.99 compared to 0.76. This indicates that the Random Forest Regressor model provided a better fit to the data and could make more accurate predictions regarding mental fitness.

--> In conclusion, the "Mental Fitness Tracker" project successfully developed an AI-driven system to monitor and support mental well-being. The project demonstrated the effectiveness of using regression models in predicting mental fitness based on the provided dataset. The results indicated the potential of the Random Forest Regressor model for accurate mental fitness tracking. This project opens avenues for further research and development in the field of mental health using artificial intelligence.

• The "Mental Fitness Tracker" project aimed to develop an AI-powered system to monitor and support mental well-being using regression models.
• The dataset was preprocessed by handling missing values and performing label encoding on categorical variables.
• Exploratory data analysis techniques such as heatmaps, pair plots, and line plots were used to gain insights into the relationships between variables and mental fitness.
• The dataset was split into training and testing sets, and both Linear Regression and Random Forest Regressor models were trained and evaluated.
• The Random Forest Regressor model outperformed the Linear Regression model, achieving a higher score of 0.99 compared to 0.76.
• The project demonstrated the potential of using regression models for accurate mental fitness tracking and opens avenues for further research in the field of mental health using artificial intelligence.

• IBM SkillsBuild Artificial Intelligence (AI) Internship

  I express my heartfelt gratitude to the mentors and instructors who guided me throughout this enriching journey and provided valuable insights into the world of AI. I extend my sincere thanks to IBM and AICTE for organizing this initiative, enabling students like me to explore the practical applications of AI.

• Frameworks and Dependencies: