1.
WHAT IS SVM? WHAT PARAMETERS YOU WILL
NEED TO TUNE DURING MODEL TRAINING?
HOW IS DIFFERENT KERNEL CHANGING THE
CLASSIFICATION RESULT?
Support Vector Machine (SVM) is a supervised machine learning algorithm used for classification and regression tasks. The goa l of
SVM is to find the best boundary (a hyperplane) that separates the data points into different classes, while maximizing the m argin
between the data points and the boundary.
During model training, there are several parameters that need to be tuned to improve the performance of SVM:
1.C: The C parameter controls the trade-off between maximizing the margin and minimizing the classification error. A smaller C value
will result in a larger margin, but may result in more misclassifications, while a larger C value will result in a smaller ma rgin, but may
result in better classification performance.
2.Gamma: The gamma parameter controls the width of the radial basis function (RBF) kernel. A larger gamma value results in a
narrower RBF, which results in a more complex decision boundary, while a smaller gamma value results in a wider RBF, which re sults
in a simpler decision boundary.
3.Kernel: SVM can use different types of kernels to model the decision boundary. The most commonly used kernels are linear,
polynomial, and radial basis function (RBF) kernels. Changing the kernel can have a significant impact on the classification
performance. A linear kernel is appropriate for linearly separable data, while a non-linear kernel such as polynomial or RBF is
appropriate for non-linearly separable data.
4.Degree: If polynomial kernel is used, degree parameter controls the degree of the polynomial. Higher degree leads to more com plex
decision boundaries.
5.Coef0 : controls the influence of higher degree polynomials in the kernel function.
By tuning these parameters, the performance of SVM can be improved and the best parameters can be selected that result in the
highest classification accuracy.
20XX Pitch Deck 1

2.
HOW DOES A NEURAL NETWORK WITH ONE
LAYER AND ONE INPUT AND OUTPUT
COMPARE TO LOGISTIC REGRESSION?
A neural network with one layer and one input and output is similar to logistic regression in that they both are used for binary
classification tasks. Both methods take input features, apply some mathematical operations on it and produce an output.
However, there are some key differences between the two methods:
1.Complexity: Logistic regression is a linear model, meaning that it can only model linear relationships between the input
features and the output. In contrast, a neural network with one layer can be considered as a non-linear model and can model
complex non-linear relationships.
2.Model flexibility: Logistic regression has a fixed form, meaning that it can only model a specific type of relationship
between the input features and the output, while a neural network with one layer can be adjusted to model different types of
relationships by changing the number of neurons in the layer.
3.Number of parameters: Logistic regression has a fixed number of parameters (coefficients for each input feature), which
can be determined by training the model on a dataset. In contrast, a neural network with one layer has a number of
parameters that can be adjusted by training the model, which allows for greater flexibility in modeling the data.
4.Non-linearity: Logistic Regression is a linear model, it can only model linear relationships between the input features and
the output. Neural networks, on the other hand, can model non-linear relationships between the inputs and outputs by using
non-linear activation functions.
In general, a neural network with one layer and one input and output can be considered as a non -linear extension of logistic
regression, and can be more powerful in modeling complex non-linear relationships between the inputs and outputs.
20XX Pitch Deck 2

3.
HOW TO MAXIMISE PROFIT BASED ON THE
OUTCOMES OF DATA ANALYSIS?
Maximizing profit based on the outcomes of data analysis typically involves several steps:
1.Identify key performance indicators (KPIs) that are most closely tied to profitability. These may include metrics such as rev enue,
customer acquisition costs, and lifetime value of a customer.
2.Collect and analyze data related to these KPIs. Use tools such as spreadsheet software, business intelligence (BI) platforms, or data
visualization tools to organize and make sense of the data.
3.Identify patterns and trends in the data that can inform business decisions. Look for correlations between different metrics and try to
identify any causal relationships that may exist.
4.Use the insights gained from the data analysis to inform business decisions. For example, if the data shows that a particular
marketing campaign is driving a high volume of new customers at a low cost, consider investing more in that campaign to maxim ize
profits.
5.Continuously monitor and analyze data to stay informed about the performance of the business, and adjust strategies according ly.
6.Optimize pricing strategies based on customer segmentation, market conditions, and costs.
7.Identify and target the most profitable customers, products, and channels.
8.Continuously monitor and improve operational efficiency and reduce costs.
9.Use data-driven insights to innovate and develop new products or services that will increase revenue.
10.Continuously evaluate, track and measure the results and outcomes of the actions taken based on data analysis and make
adjustments accordingly.
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4.
WHAT'S YOUR APPROACH TO SCALING
DATA SCIENCE THROUGHOUT AN
ORGANIZATION?
My approach to scaling data science throughout an organization would involve several key steps:
1.Establish a clear vision and strategy: Develop a clear vision and strategy for how data science can support the organization' s goals
and objectives. This vision should be communicated to all stakeholders, including senior management and other departments.
2.Build a strong data science team: Assemble a team of data scientists, engineers, and business analysts with diverse skills an d
backgrounds to ensure that the data science initiative is well-rounded.
3.Invest in technology and infrastructure: Invest in the necessary technology and infrastructure to support data science effort s, such as
cloud computing, data storage, and analytical tools.
4.Define governance and data management practices: Establish governance and data management practices to ensure that data is
accurate, reliable, and accessible. This includes data quality checks, data access controls, and data security.
5.Develop partnerships and collaborations: Develop partnerships and collaborations with other departments and stakeholders to h elp
identify opportunities for data science and to facilitate the integration of data science into decision -making processes.
6.Create a culture of data-driven decision making: Promote a culture of data-driven decision making, where data science is integrated
into the day-to-day operations of the organization. This includes providing data literacy training and encouraging experimentation and
exploration.
7.Measure and monitor performance: Measure and monitor the performance of data science initiatives to ensure that they are
delivering value to the organization. Use these metrics to inform and improve future data science efforts.
8.Continuously improve: Continuously evaluate and improve the data science process to ensure that it remains aligned with the
organization's goals and objectives and is delivering the desired results.
20XX Pitch Deck 4