Screening of Mental Health in Adolescents using ML.pptx
- 1. Screening of Mental Healthin adolescentsusing ML PRESENTED BY- Ritik Raj Mehta(12000119106) Preyoshi Sen(12000119094) Nitish Kumar(12001319032) GUIDED BY- Mr Bappaditya Das
- 2. CONTENTS WHAT IS MACHINE LEARNING? TYPES OF MACHINE LEARNING CLASSIFICATION MODEL IN MACHINE LEARNING Logistic Regression WHAT IS CLASSIFICATION ALGORITHM? USES OF CLASSIFICATION ALGORITHMS SOFTWARE REQUIREMENTS PYTHON LIBRARIES WHAT IS THE DNN CLASSIFIER? CONCLUSION REFERENCES
- 3. WHAT IS MACHINE LEARNING? • Machine learning commonly referred to as ML powers some of the most important technologies we use right from translation apps in our mobile phones to autonomous vehicles. This article explains the core concepts behind Machine Learning. Machine Learning offers a new way to solve problems and answer complex questions. In basic terms, Machine Learning is the process of training a piece of software, called a model, to make useful predictions from data. A Machine Learning model represents the mathematical relationship between the elements of data that a Machine Learning system uses to make predictions.
- 4. WHAT ARE THE DIFFERENT TYPES OF MACHINE LEARNING? Machine Learning algorithms run on various programming languages and techniques. However, these algorithms are trained using various methods, out of which three main types of Machine learning are: •Supervised Learning •Unsupervised Learning •Reinforcement Learning
- 5. CLASSIFICATION MODEL IN MACHINE LEARNING Basic Concepts Supervised Learning is defined as the category of data analysis where the target outcome is known or labelled e.g. whether the customer(s) purchased a product, or did not. However, when the intention is to group them based on what all each purchased, then it becomes Unsupervised. This may be done to explore the relationship between customers and what they purchase. Classification and Regression both belong to Supervised Learning, but the former is applied where the outcome is finite while the latter is for infinite possible values of outcome (e.g. predict $ value of the purchase).
- 7. Logistic Regression Logistic Regression utilizes the power of regression to do classification and has been doing so exceedingly well for several decades now, to remain amongst the most popular models. One of the main reasons for the model’s success is its power of explainability i.e. calling-out the contribution of individual predictors, quantitatively.
- 8. WHAT IS CLASSIFICATION ALGORITHM? Based on training data, the Classification algorithm is a Supervised Learning technique used to categorize new observations. In classification, a program uses the dataset or observations provided to learn how to categorize new observations into various classes or groups. For instance, 0 or 1, red or blue, yes or no, spam or not spam, etc.
- 9. USES OF CLASSIFICATION ALGORITHMS Classification algorithms can be used in different places. Below are some popular use cases of Classification Algorithms: 1.Email Spam Detection 2.Speech Recognition 3.Identifications of Cancer, Tumour cells. 4.Drugs Classification 5.Biometric Identification, etc.
- 10. CLASSIFICATION ALGORITHMS What are Classification Algorithms and the different algorithms used in the prediction? Classification is the process of recognizing, understanding, and grouping ideas and objects. The Classification Algorithms used in the prediction are: K-nearest Neighbors - K-nearest Neighbors (k-NN) is a pattern recognition algorithm that uses training datasets to find the k closest relatives in future examples. When k-NN is used in classification, you calculate to place data within the category of its nearest neighbour. Using, GridSearchCV we try every combination of a present list of values of the hyper-parameters and choose the best combination based on the cross validation score and RandomSearchCV tries random combinations of a range of values (we have to define the number of iterations).
- 11. WHAT IS THE DNN CLASSIFIER? Deep neural networks (DNN) can be defined as ANNs with additional depth, that is, an increased number of hidden layers between the input and the output layers. Deep Neural Networks (DNNs have become a promising solution to inject AI in our daily lives from self-driving cars, smartphones, games, drones, etc. INPUT The input to a DNN can include: 1. dense features 2. sparse features
- 12. SOFTWARE REQUIREMENTS ANACONDA DISTRIBUTION: Anaconda is a free and open-source distribution of the Python programming languages for scientific computing (data science, machine learning applications, large-scale data processing, predictive analytics, etc.), that aims to simplify package management system and deployment.
- 13. PYTHON LIBRARIES: 1.SKLEARN: It features various classification, regression, and clustering. Algorithms including support vector machines, random forests, and gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy. 2.NUMPY: NumPy is a general-purpose array-processing package. It provides a high-performance multidimensional array object, and tools for working with these arrays. It is the fundamental package for scientific computing with Python.
- 14. 3. PANDAS: Pandas is one of the most widely used python libraries in data science. It provides high-performance, easy to use structures and data analysis tools. Unlike NumPy library which provides objects for multi-dimensional arrays, Pandas provides in-memory 2d table object called Data frame. 4. Matplotlib: Matplotlib is a powerful tool for executing a variety of tasks.It is able to create different types of visualization reports like line plots, scatter plots, histograms, bar charts, pie charts, box plots, and many more different plots. Matplotlib is a cross-platform, data visualization and graphical plotting library for Python and its numerical
- 16. CONCLUSION As there are many available techniques of machine learning, it is very important to compare those techniques and then identify the best among them that will suit the domain of interest. Nowadays, we have many special programs in the medical field that predict disease very accurately in advance so that treatment can be done effectively and efficiently. In this proposed work we have compared different techniques of machine learning which are used to classify the dataset on various problems of mental health.
- 17. REFERENCES 1.https://www.geeksforgeeks.org/introduction-machine-learning/ 2.https://analyticsindiamag.com/7-types-classification-algorithms/ 3.BOOK: Advanced Machine Learning; AUTHORS: (a) Rajiv Chopra, Deep Learning, Khanna Book Publishing Co., New Delhi (b) V.K. Jain, Machine Learning, Khanna Book Publishing Co., New Delhi 4.https://www.w3schools.com/datascience/ds_analyze_data.asp 5.https://www.analyticsvidhya.com/blog/2020/11/popular-classification models- for-machine-learning