![](bg5.png)
hypothesis is rejected and accepts the alternative
hypothesis.
From the above result, it shows that above 4
variables are significant as in [Table 1]. The larger the
chi-squared test statistic, the more likely it is that the
observed frequencies are different from the expected
frequencies. Now, we try another approach called
‘Feature importance using Decision tree classifier’ to
identify the variables responsible for crypto currency
ownership. The output is given in a visualisation from
as in [Fig 3] as well as in a tabular form [Table 2].
This second approach of identifying independent
variables responsible for Crypto ownership using the
feature importance result using Decision Tree
classier, gives us following 4 variables are significant.
Comparing both the approaches [Chi-square and
Decision tree classifier] for identifying the
parameters which has an impactful relationship on
crypto asset ownership, below are 2 features
commonly showing Significance is given in [Table
3]. Finally, we wanted to confirm if there are any
correlations exist between these six significant
variables and we create the correlation matrix
heatmap using Pearson method to find out
connectedness amongst them in the below [Fig 4].
From the above matrix, it is obvious that none of
the independent features are correlated with one
another. Therefore, there is no biasedness. We also
performed ‘catboost’ algorithm, based on gradient
boosting decision trees to validate the factors
identified above and got the results with 97.87% of
accuracy. Hence our prediction model is more
accurate.
To summarise the findings, data analysis using
Python’s statistical tools 1) Chi-square test, 2)
Decision tree classifier 3) Correlation Matrix and 4)
catboost algorithm we conclude that, the following
six variables have more significance relationship that
has an influence on the cryptocurrency investment
are, 'long-term savings investment strategy', 'highest
educational stream', 'percentage of long-term savings
in cryptocurrency', 'monthly income range',
‘Profitable investing in stock or cryptocurrency' and
'participate in training'.
Conclusion: This research study has provided an
insight into the possible factors triaged to the
cryptocurrency investment amongst the women
teaching fraternity with their level of significant
relationship. Similarly, Python language is also
shown as a data analysis & visualization tool, further
it can also be used for Quantitative modeling and
Algorithmic trading in cryptocurrencies. This study is
also helpful to investors choosing cryptocurrency as
their long-term savings; especially women who are
more aggressive in savings as they tend to plan for
their long- term investment when it comes to
retirement, due to their longer life expectancy, breaks
in professional life, early retirement and most
importantly gender pay gap.
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Women Teachers and Their Long-Term Savings Approach on Cryptocurrency Investment
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