├── 423_Manglam Vats.pdf ├── 423_Sphoorthi B Savalgi_certificate.pdf ├── Gmail - Acceptance Notification - IEEE 5th INCET 2024.pdf ├── IEEE_Conference_Template__4_.pdf ├── LICENSE ├── ML_latest.ipynb └── README.md /423_Manglam Vats.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Vatsmanglam/Mental-Health-Prediction-Using-ML-Algorithms/be4e5e3d267267866ca29e440f5b28b783c46f09/423_Manglam Vats.pdf -------------------------------------------------------------------------------- /423_Sphoorthi B Savalgi_certificate.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Vatsmanglam/Mental-Health-Prediction-Using-ML-Algorithms/be4e5e3d267267866ca29e440f5b28b783c46f09/423_Sphoorthi B Savalgi_certificate.pdf -------------------------------------------------------------------------------- /Gmail - Acceptance Notification - IEEE 5th INCET 2024.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Vatsmanglam/Mental-Health-Prediction-Using-ML-Algorithms/be4e5e3d267267866ca29e440f5b28b783c46f09/Gmail - Acceptance Notification - IEEE 5th INCET 2024.pdf -------------------------------------------------------------------------------- /IEEE_Conference_Template__4_.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Vatsmanglam/Mental-Health-Prediction-Using-ML-Algorithms/be4e5e3d267267866ca29e440f5b28b783c46f09/IEEE_Conference_Template__4_.pdf -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2024 Manglam 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | Mental-Health-Prediction-Using-ML-Algorithms 2 | 3 | This project aims to predict mental health issues using various machine learning algorithms. The goal is to develop models that can accurately identify individuals who may be at risk of mental health problems based on provided data. 4 | 5 | 6 | 7 | 8 | 9 | Project Description 10 | 11 | Mental health is a critical aspect of overall well-being, and early identification of potential mental health issues can lead to timely intervention and support. In this project, we explore the application of machine learning algorithms to predict mental health conditions. The dataset used contains various features related to demographics, lifestyle, and other relevant factors. 12 | 13 | 14 | **Key Features** 15 | 16 | **.** Data Preprocessing: Handling missing values, encoding categorical variables, and scaling features. 17 | 18 | **.** Exploratory Data Analysis (EDA): Understanding the data distribution, relationships, and key insights through visualizations. 19 | 20 | **.** Model Building: Implementing multiple machine learning algorithms, including logistic regression, decision trees, random forests, support vector machines (SVM), and more. 21 | 22 | **.** Model Evaluation: Assessing the performance of models using metrics such as accuracy, precision, recall, F1-score, and ROC-AUC. 23 | 24 | **.** Hyperparameter Tuning: Optimizing model performance through techniques such as grid search and random search. 25 | 26 | 27 | **Installation** 28 | 29 | To run this project, you need to install the required dependencies. Use the following command to install them: 30 | 31 | pip install -r requirements.txt 32 | 33 | **Usage** 34 | 35 | git clone https://github.com/your-username/mental-health-prediction-ml.git 36 | cd mental-health-prediction-ml 37 | 38 | 39 | **Open the Jupyter Notebook** 40 | 41 | jupyter notebook Mental_Health_Prediction.ipynb 42 | 43 | 44 | **License** 45 | 46 | This project is licensed under the MIT License - see the LICENSE file for details. 47 | 48 | **Acknowledgements** 49 | 50 | **.** Dataset Source: https://github.com/cdodiya/Mental-Health-Prediction-using-Machine-Learning-Algorithms/blob/main/survey.csv. 51 | 52 | **.** Inspiration and support from the community and mentors. 53 | 54 | 55 | --------------------------------------------------------------------------------