GOOD

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

15. ANALYSIS AND DESIGN

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