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PythagoraSwitch mini: Framy, Algorithm March with the Ninjas
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StatQuest: Logistic Regression
⏲ 8 minutes 48 seconds 👁 1M
Logistic Regression in 3 Minutes
⏲ 3 minutes 48 seconds 👁 27K
How to Forecasting sales-based expenses?
⏲ 4:22 👁 40K
Logistic Regression [Simply explained]
⏲ 14 minutes 22 seconds 👁 65.7K
Machine Learning Tutorial Python - 8:Logistic Regression (Binary Classification)
⏲ 19 minutes 19 seconds 👁 271.4K
How to Calculation Fixed costs and variable costs in Financial Modeling?
⏲ 3:17 👁 35K
Logistic Regression | Logistic Regression in Python | Machine Learning Algorithms | Simplilearn
⏲ 38 minutes 17 seconds 👁 88.6K
Locally Weighted & Logistic Regression | Stanford CS229: Machine Learning - Lecture 3 (Autumn 2018)
⏲ 1 hour 19 minutes 34 seconds 👁 251K
Session 13: Leveraging AI and Machine Learning in Open RAN | Revolutionizing Networks
⏲ 4:15 👁 25K
Logistic Regression Machine Learning Example | Simply Explained
⏲ 6 minutes 37 seconds 👁 32.7K
Correlation Vs Regression: Difference Between them with definition & Comparison Chart
⏲ 7 minutes 51 seconds 👁 163.2K
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_pythagoraswitch-mini-framy-algorithm-march-with-the-ninjas.webp "With Synchronized Trigger Machine (Patranger) and Pepsiman Commercial")
_statquest-logistic-regression.webp "StatQuest with Josh Starmer")
_logistic-regression-in-3-minutes.webp "3-Minute Data Science")
_how-to-forecasting-sales-based-expenses.webp "Forecasting sales-based expenses involves predicting the expenses that are directly tied to the level of sales a business generates. These expenses typically include costs such as commissions, advertising, marketing, and sometimes production costs that vary directly with sales volume. Here's a step-by-step guide on how to forecast sales-based expenses:<br/><br/>Understand Historical Data: Review historical sales data and corresponding expenses to identify patterns and trends. Look for correlations between sales volumes and related expenses.<br/><br/>Identify Key Drivers: Determine the key factors that drive sales-based expenses. This could include sales volume, market trends, promotional activities, seasonality, and any other factors that directly impact expenses.<br/><br/>Develop Sales Forecasts: Generate sales forecasts based on a combination of historical data, market research, industry trends, and internal factors such as marketing strategies and sales projections. Use quantitative methods like time-series analysis, regression analysis, or qualitative methods like expert opinions and market research to forecast sales.<br/><br/>Estimate Expense Ratios: Calculate expense ratios by analyzing historical data to determine the percentage of sales that each expense category typically represents. For example, if historically marketing expenses have been 10% of sales, you can use this ratio to estimate future marketing expenses based on forecasted sales.<br/><br/>Adjust for Changes: Consider any changes in business operations, market conditions, or other factors that may impact sales-based expenses. Adjust your expense ratios accordingly to reflect these changes in your forecasts.<br/><br/>Review and Refine: Regularly review and refine your forecasts based on actual sales performance and expenses. Compare your forecasts to actual results and identify any discrepancies. Use this information to improve the accuracy of future forecasts.<br/><br/>Scenario Analysis: Conduct scenario analysis to assess the potential impact of different sales scenarios on expenses. This allows you to prepare for various outcomes and make informed decisions based on different sales projections.<br/><br/>Communication and Collaboration: Ensure collaboration between sales, marketing, finance, and other relevant departments to gather insights and validate assumptions. Effective communication helps in aligning expectations and improving the accuracy of forecasts.<br/><br/>By following these steps and employing both quantitative and qualitative methods, businesses can develop more accurate forecasts for sales-based expenses, enabling better financial planning and decision-making.")
_logistic-regression-simply-explained.webp "DATAtab")
_machine-learning-tutorial-python-8-logistic-regression-binary-classification.webp "codebasics")
_how-to-calculation-fixed-costs-and-variable-costs-in-financial-modeling.webp "In financial modeling, calculating fixed costs and variable costs involves identifying the costs that remain constant regardless of production or sales volume (fixed costs) and those that vary with production or sales volume (variable costs). Here's how you can calculate them:<br/><br/>Identify Fixed Costs:<br/><br/>Fixed costs are expenses that do not change with the level of production or sales. They remain constant within a certain range of activity.<br/>Examples of fixed costs include rent, salaries of permanent staff, insurance premiums, depreciation, and property taxes.<br/>To calculate fixed costs, review the company's financial statements and identify expenses that are consistent over time and not directly tied to production or sales volume.<br/>Calculate Variable Costs:<br/><br/>Variable costs are expenses that change in proportion to the level of production or sales. As production increases, variable costs also increase, and vice versa.<br/>Examples of variable costs include raw materials, direct labor, sales commissions, packaging costs, and shipping expenses.<br/>To calculate variable costs, you can use historical data to determine the variable cost per unit or the variable cost as a percentage of sales revenue.<br/>Segregate Mixed Costs:<br/><br/>Some costs may have elements of both fixed and variable components, known as mixed costs.<br/>To segregate mixed costs into their fixed and variable components, you can use techniques like the high-low method, scattergraph method, or regression analysis.<br/>The high-low method involves selecting the highest and lowest activity levels and corresponding costs and then calculating the variable cost per unit and the total fixed cost.<br/>Build a Financial Model:<br/><br/>Incorporate the calculated fixed costs and variable costs into your financial model.<br/>Use formulas or functions in spreadsheet software to represent fixed costs and variable costs in your model.<br/>For example, you can use the SUM function to aggregate fixed costs, while multiplying the variable cost per unit by the level of activity (e.g., units sold) to calculate variable costs.<br/>Sensitivity Analysis:<br/><br/>Conduct sensitivity analysis to assess the impact of changes in production or sales volume on total costs.<br/>By varying the assumptions related to fixed costs and variable costs, you can analyze how different scenarios affect the company's profitability and financial performance.<br/>By accurately calculating fixed costs and variable costs in your financial model, you can better understand cost structures, conduct scenario analysis, and make informed decisions regarding pricing, production levels, and resource allocation.")
_logistic-regression-124-logistic-regression-in-python-124-machine-learning-algorithms-124-simplilearn.webp "Simplilearn")
_locally-weighted-amp-logistic-regression-124-stanford-cs229-machine-learning-lecture-3-autumn-2018.webp "Stanford Online")
_session-13-leveraging-ai-and-machine-learning-in-open-ran-124-revolutionizing-networks.webp "Welcome to Session 13 of our Open RAN series! In this session, we'll explore the integration of Artificial Intelligence (AI) and Machine Learning (ML) in Open RAN. These technologies play a crucial role in designing intelligent systems that enhance the overall ecosystem of Open RAN.<br/><br/>Artificial Intelligence and Machine Learning<br/>Artificial Intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think and learn like humans. Machine Learning (ML) is a subset of AI that allows machines to learn from data without being explicitly programmed. These technologies can revolutionize Open RAN by enabling intelligent decision-making, predictive maintenance, and network optimization.<br/><br/><br/>Application of Machine Learning in Open RAN<br/>Machine Learning algorithms use mathematical functions to analyze data and make predictions or decisions based on that analysis. In Open RAN, ML can be applied to various areas such as network optimization, predictive maintenance, and intelligent resource allocation. ML algorithms can analyze network traffic patterns, predict equipment failures, and optimize network performance, leading to improved efficiency and reliability.<br/><br/><br/>Join us as we explore the potential of AI and Machine Learning in Open RAN. Don't forget to subscribe to the \")
_logistic-regression-machine-learning-example-124-simply-explained.webp "Coding Lane")
_correlation-vs-regression-difference-between-them-with-definition-amp-comparison-chart.webp "Key Differences")
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