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Purpose: Watershed development projects create avenues for improving the productivity of areas which are rainfed or degraded through watershed programmes. Few Key Performance Indicators (KPIs) of the watershed projects are: conversion of degraded or rainfed land into agricultural land, area where soil moisture activities, horticulture/afforestation activities and protective irrigation activities are achieved.

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Objectives: Cost Benefit Analysis (CBA) helps to identify the economic outcome of a process.

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Methodology/Design/Approach: Analysis can be performed using Key Performance Indicators (KPIs) to know about the costs and the benefits. CBA can also be visualised using both metric and non-metric features such as problem specific KPIs.

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Findings/Result: CBA through data visualisation using Power Bi and Python language on the dataset obtained from WDC-PMKSY 2.0 portal identified a greater correlation among project cost, area and number of projects. 35.98% of contribution was observed from conversion of degraded/rainfed land, while protective irrigation achieved 14.88% during the two years after initiation of the watershed projects. Karnataka has recorded 35.19% of contribution towards the total benefited project areas in the Country.

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Originality/Value: Each of the 4 area related KPIs of the watershed projects assigned were studied to determine their encouragement towards the state's development for the period 2021-2023. To group states that demonstrated similar patterns in KPIs achievement, Machine Learning algorithms PAM, K-means clustering, Minibatch KMeans and Birch clustering were implemented. Agglomerative clustering produced two clusters while the other algorithms created 3 clusters and grouped Assam, Karnataka, Madhya Pradesh, Odisha as the highest benefiting states as one cluster.

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Keywords: Cost Benefit Analysis, Watershed Projects, KPIs, Data Visualization, Machine Learning.

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