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ABSTRACT
Professionals can help diagnose and treat patients more effectively by detection
mental health issues early. In this article, we discuss the current status of AI in the
mental health field and its potential applications in healthcare. Machine learning
techniques can help address the basic mental health issues that people face, such
as anxiety and depression. They can also detect patterns and provide helpful
suggestions for addressing the problems. The attribute data has been reduced
using Feature Selection algorithms. Various machine learning algorithms have
been compared in terms of accuracy over the full set of attributes and a select set
of attributes. Although various algorithms have been studied, further work is still
needed to reduce the aperture between AI and mental health analysis.
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TABLE OF CONTENT
SL.NO
TITLE
PAGE NO.
BONAFIDE CERTIFICATE
I
DECLARATION
II
ACKNOWLEGEMENT
III
ABSTRACT
IV
1
INTRODUCTION
1.1 DATA SCIENCE
1.1.1 DATA SCIENTIST
1.2 ARTIFICIAL INTELLIGENCE
1.3 NATURAL LANGUAGE PROCESSING (NLP)
1.4 MACHINE LEARNING
8
2
PREPARING DATASET
13
3
PROPOSED SYSTEM
3.1 DATA WRANGLING
3.2 DATA COLLECTION
3.3 BUILDING THE CLASSIFICATION MODEL
3.4 ADVANTAGES
14
4
LITERATURE SURVEY
4.1 REVIEW OF LITERATURE SURVEY
16
5
METHODOLOGY
5.1 OBJECTIVES
5.2 PROJECT GOAL
5.3 SCOPE OF THE PROJECT
5.4 LIST OF MODULES
21
6
FEASIBILITY STUDY
6.1 DATA WRANGLING
6.2 DATA COLLECTION
6.3 PREPROCESSING
6.4 BUILDING THE CLASSIFICATION MODEL
6.5 CONSTRUCTION OF A PREDICTIVE MODEL
23
7
PROJECT REQUIREMENTS
7.1 FUNCTIONAL REQUIREMENTS
7.2 NON-FUNCTIONAL REQUIREMENTS
7.3 TECHNICAL REQUIREMENTS
25
8
SOFTWARE DESCRIPTION
8.1 ANACONDA NAVIGATOR
8.2 JUPYTER NOTEBOOK
8.3 WORKING PROCESS
8.4 PYTHON
27
9
SYSTEM ARCHITECTURE
9.1 WORKFLOW DIAGRAM
34
10
MODULE DESCRIPTION
10.1 DATA PREPROCESSING
10.2 EXPLORATION DATA ANALYSIS OF
36
7
VISUALIZATION
10.3 COMPARING ALGORITHM WITH
PREDICTION IN THE FORM OF BEST ACCURACY
RESULT
10.4 PREDICTION RESULT BY ACCURACY
10.5 ALGORITHM AND TECHNIQUE
EXPLANATION
10.5.1 LOGISTIC REGRESSION
10.5.2 RANDOM FOREST CLASSIFIER
11
CONCLUSION AND FUTURE WORK
48
12
APPENDICES
A. SAMPLE CODE
B. SCREENSHOTS
C. REFERENCES
49
LIST OF FIGURES
SL.NO
TITLE
PAGE.NO
1
PROCESS OF MACHINE LEARNING
XII
2
ARCHITECTURE OF PROPOSED MODEL
XV
3
PROCESS OF DATAFLOW DIAGRAM
XXIV
4
ANACONDA NAVIGATOR (1)
XXVIII
5
ANACONDA NAVIGATOR (2)
XXIX
6
SYSTEM ARCHITECTURE
XXXIV
7
WORKFLOW DIAGRAM
XXXV
8
BEFORE PREPROCESSING
XXXVII
9
AFTER PREPROCESSING
XXXVII
10
DATA TYPE IDENTIFICATION
XXXVIII
11
MODULE (1)
XXXVIII
12
AGE DISTRIBUTION
XXXIX
13
MEMBERS IN FAMILY VS DEPRESSION
XL
14
MODULE (2)
XL
15
CLASSIFICATION REPORT OF LOGISTIC
REGRESSION
XLIV
16
CONFUSION MATRIX OF LOGISTIC REGRESSION
XLV
17
MODULE (3)
XLV
18
CLAASIFICATION REPORT OF RANDOM FOREST
XLVI
19
CONFUSION MATRIX OF RANDOM FOREST
XLVII
20
MODULE (4)
XLVII
