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Figure: 19 Implementing Support Vector Machine Model e Figure 19 below shows the algorithm for implementing the SVM model on our dataset.

Figure 19 19 Implementing Support Vector Machine Model e Figure 19 below shows the algorithm for implementing the SVM model on our dataset.

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Figure: 2 Importing the Libraries and Dataset
Figure: 2 Importing the Libraries and Dataset
As you can see in figure 3 that there are only few entries on left most part of part of the graph, so for normalizing what we did is we eliminated the lowest 0.01 percentile data to make it a normal distribution. Figure: 3 Before & After Data Distribution of Age Parameter
As you can see in figure 3 that there are only few entries on left most part of part of the graph, so for normalizing what we did is we eliminated the lowest 0.01 percentile data to make it a normal distribution. Figure: 3 Before & After Data Distribution of Age Parameter
Figure: 4 Data Distribution of Height Parameter As you can see height parameter is evenly distributed as you can see in figure 4, so there is no need of normalizing
Figure: 4 Data Distribution of Height Parameter As you can see height parameter is evenly distributed as you can see in figure 4, so there is no need of normalizing
As you can see in figure 5 that the graph is extended from the bottom part on the right, so we simply eliminated top 0.01 percentile data to make it a normal distribution. Figure: 5 Before & After Data Distribution of Weight Parameter
As you can see in figure 5 that the graph is extended from the bottom part on the right, so we simply eliminated top 0.01 percentile data to make it a normal distribution. Figure: 5 Before & After Data Distribution of Weight Parameter
Figure: 6 Original Distribution of AP_HI Parameter
Figure: 6 Original Distribution of AP_HI Parameter
As you can see in figure 6 that the original graph is extended from both the sides, so, we eliminated both top and lowest 0.01 percentile to achieve normal distribution as shown in figure 7.
As you can see in figure 6 that the original graph is extended from both the sides, so, we eliminated both top and lowest 0.01 percentile to achieve normal distribution as shown in figure 7.
As you can see in figure 8 that the original graph is extended from right side so, we eliminated both top and 0.014 percentile to achieve normal distribution. Also, this is the last parameter for normalizing as there are no more value based parameters remaining. Figure: 8 Before and After Data Distribution of AP_LO Parameter
As you can see in figure 8 that the original graph is extended from right side so, we eliminated both top and 0.014 percentile to achieve normal distribution. Also, this is the last parameter for normalizing as there are no more value based parameters remaining. Figure: 8 Before and After Data Distribution of AP_LO Parameter
Figure: 12 Logistic Regression Training Accuracy Code
Figure: 12 Logistic Regression Training Accuracy Code
Figure 11 below shows the algorithm for implementing the logistic regression model on our dataset. Figure: 10 Logistic Regression Formula
Figure 11 below shows the algorithm for implementing the logistic regression model on our dataset. Figure: 10 Logistic Regression Formula
Figure: 13 Logistic Regression Testing Accuracy Code
Figure: 13 Logistic Regression Testing Accuracy Code
Figure: 15 Implementing Naive Bayes Classifier Model
Figure: 15 Implementing Naive Bayes Classifier Model
Figure: 17 Implementing Decision Tree Classifier Model e Figure 17 below shows the algorithm for implementing the Decision Tree model on our dataset.
Figure: 17 Implementing Decision Tree Classifier Model e Figure 17 below shows the algorithm for implementing the Decision Tree model on our dataset.
Figure: 21 Implementing Random Forest Model
Figure: 21 Implementing Random Forest Model
Figure: 20 Random Forest Model
Figure: 20 Random Forest Model
A. Accuracy Comparison
A. Accuracy Comparison
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