ITC Analytics Accelerator - Revision history

Raj: /* General */

2024-08-14T09:44:21Z

General

← Older revision		Revision as of 09:44, 14 August 2024
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	* var['Column Title'][0:5] \| First 5 rows are shown		* var['Column Title'][0:5] \| First 5 rows are shown
	* var[['Column Title','Column Title']][0:5]		* var[['Column Title','Column Title']][0:5]
			* header = list(data.columns.values) \| capture the column names (header)
	* var.sample(3) \| Random sampling of any 3		* var.sample(3) \| Random sampling of any 3
	* var.describe(include = 'all')		* var.describe(include = 'all')
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	</source>		</source>

	=== Plotting ====		=== Plotting ====
	<source lang=python>		<source lang=python>

2024-08-14T09:31:33Z

Communication of Data Science projects

← Older revision		Revision as of 09:31, 14 August 2024
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	** High specificity -> High true negatives		** High specificity -> High true negatives
	*** High sensitivity = True Negatives /(True Negatives+ False positives)		*** High sensitivity = True Negatives /(True Negatives+ False positives)
	== Communication of Data Science projects ==		== Communication in Data Science projects ==

	== Code ==		== Code ==

2024-08-14T09:02:17Z

Model Performance Testing / Model evaluation

← Older revision		Revision as of 09:02, 14 August 2024
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	** High specificity -> High true negatives		** High specificity -> High true negatives
	*** High sensitivity = True Negatives /(True Negatives+ False positives)		*** High sensitivity = True Negatives /(True Negatives+ False positives)
			== Communication of Data Science projects ==

	== Code ==		== Code ==

2024-08-14T09:01:07Z

Model Performance Testing

← Older revision		Revision as of 09:01, 14 August 2024
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	== Model Performance Testing ==		== Model Performance Testing / Model evaluation ==
			Model evaluation should be part of the data driven project matrix pipeline
	* Confusion Matrix		* Confusion Matrix
	** To Identify false positives & true negatives		** To Identify false positives & true negatives
	** Precision = True positives /(True positives + False Positives) \|		** Precision = True positives /(True positives + False Positives) \|
	** Recall = True positives / (True positives + False Negatives) \| In a case -> Identify the person with heart disease		** Recall = True positives / (True positives + False Negatives) \| In a case -> Identify the person with heart disease
	** High sensitivity -> High true ~~positivies~~		** High sensitivity -> High true positives
	*** High sensitivity = True positives /(True positives + False Negatives)		*** High sensitivity = True positives /(True positives + False Negatives)
	** High specificity -> High true negatives		** High specificity -> High true negatives

2024-08-14T08:59:26Z

Model Performance Testing

← Older revision		Revision as of 08:59, 14 August 2024
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	** Precision = True positives /(True positives + False Positives) \|		** Precision = True positives /(True positives + False Positives) \|
	** Recall = True positives / (True positives + False Negatives) \| In a case -> Identify the person with heart disease		** Recall = True positives / (True positives + False Negatives) \| In a case -> Identify the person with heart disease
			** High sensitivity -> High true positivies
			*** High sensitivity = True positives /(True positives + False Negatives)
			** High specificity -> High true negatives
			*** High sensitivity = True Negatives /(True Negatives+ False positives)

	== Code ==		== Code ==

2024-08-14T08:52:16Z

Workshop

← Older revision		Revision as of 08:52, 14 August 2024
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	* Logistic Regression \| Out put is a probability		* Logistic Regression \| Out put is a probability
	* OKR <- Empathy as a theme		* OKR <- Empathy as a theme


	== Model Performance Testing ==		== Model Performance Testing ==

	* Confusion Matrix		* Confusion Matrix
	** To Identify false positives & true negatives		** To Identify false positives & true negatives
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	plt.show()		plt.show()

			# Heatmap with correlation \| data should be fully numbers
			plt.figure(figsize=(9,5))
			sn.heatmap(cars_num.corr(),annot=True, linewidth=0.5)

	</source>		</source>

2024-08-14T08:49:11Z

Workshop

← Older revision		Revision as of 08:49, 14 August 2024
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	=Workshop =		=Workshop =
	* Forecast vs Prediction		* Forecast vs Prediction
	* OKR		* Logistic Regression \| Out put is a probability
			* OKR <- Empathy as a theme
	== Model Performance Testing ==		== Model Performance Testing ==

2024-08-14T08:46:29Z

Model Performance Testing

← Older revision		Revision as of 08:46, 14 August 2024
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	* Confusion Matrix		* Confusion Matrix
	** To Identify false positives & true negatives		** To Identify false positives & true negatives
	** Precision = True positives /(True positives + False Positives)		** Precision = True positives /(True positives + False Positives) \|
	** Recall = True positives / (True positives + False Negatives)		** Recall = True positives / (True positives + False Negatives) \| In a case -> Identify the person with heart disease

	== Code ==		== Code ==

2024-08-14T08:44:24Z

Model Performance Testing

← Older revision		Revision as of 08:44, 14 August 2024
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	** To Identify false positives & true negatives		** To Identify false positives & true negatives
	** Precision = True positives /(True positives + False Positives)		** Precision = True positives /(True positives + False Positives)
			** Recall = True positives / (True positives + False Negatives)

	== Code ==		== Code ==

2024-08-14T08:43:00Z

Model Performance Testing

← Older revision		Revision as of 08:43, 14 August 2024
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	* Confusion Matrix		* Confusion Matrix
			** To Identify false positives & true negatives
			** Precision = True positives /(True positives + False Positives)

	== Code ==		== Code ==