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Country: India
State: Uttar Pradesh
email id: supria.sony@suprsny.com
website: suprsny.com

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EASE 7.0 of PSBs Reforms

The EASE (Enhanced Access and Service Excellence) Reforms, overseen by the EASE Steering Committee of the Indian Bank's Association, have been fully integrated into all public sector banks (PSBs). The evolution from EASE 1.0 to EASE 6.0 has marked a significant shift, focusing on digital customer experience, analytics-driven business enhancement, technology and data-enabled capacity development, and HR operations improvement. These reforms are part of the Indian government's effort to elevate the performance and service standards of public sector banks through digital transformation and other strategic initiatives. The EASE Awards ceremony acknowledges banks for their exceptional implementation of the reform agenda. State Bank of India (SBI) secured the top position, demonstrating excellence across various parameters outlined in the EASE 6.0 framework. Journey of Ease 1.0 to Ease 7.0: In Ease 1.0 (Responsive and responsible PSBs) , the focus wa...

What is a Decision Tree?

A Decision Tree is a powerful supervised machine learning technique used for classification and regression tasks, mimicking human decision-making through a hierarchical structure of choices and outcomes, consisting of nodes, branches, and leaves. Nodes: Each node represents a feature of the dataset, such as age or income, used for decision-making. Branches: Branches indicate possible values or conditions for each feature, guiding the path based on outcomes at the nodes. Leaves: Leaves signify final outcomes or predictions, such as class labels in classification or numerical values in regression. The decision tree partitions the dataset based on conditions at each node, similar to "if-then" statements, continuing until a stopping criterion is met, like achieving homogeneity in classification or minimal improvement in regression accuracy. Key steps in constructing a decision tree include selecting the best features . Example: Predicting Whether...

Analysis of 'titanic' dataset to predict survivor traits using Logistic Regression Model

Logistic Regression is a statistical method for binary classification tasks, like yes/no or spam/non-spam or survived/not-survived. It estimates the probability of an outcome belonging to one of two classes based on input features. Unlike Linear Regression, which predicts continuous values, Logistic Regression uses a sigmoid function to generate probabilities between 0 and 1. Although called regression, it addresses a classification problem with a categorical dependent variable and employs the Maximum Likelihood Method to keep predictions within the 0 to 1 range. Equation: \[P(y=1|x) = \frac{1}{1 + e^{-(\beta_0 + \beta_1x_1 + \beta_2x_2 +...+ \beta_nx_n)}}\] where, \(P(y=1|x) \implies \) Probability of class 1 (e.g., survived) Decision threshold (e.g., 0.5) determines class The threshold value is set at 0.5. When the prediction exceeds or equal to 0.5, the logistic regression model will classify the outcome as 1; conversely, if the prediction falls below 0.5, the model wi...

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