Speaker 1: Hello everyone and welcome to this video on Predictive Analytics by Simply Learn. Are you a business professional looking to stay ahead of the competition or are you looking for a way to make data-driven decisions that can help you drive growth for your organizations? Well, there's a solution and it is Predictive Analytics. But what exactly is Predictive Analytics and how can it help you make better decisions for your business? Well, Predictive Analytics is a type of data analysis that helps organizations predict future events by identifying patterns and trends in historical data. By using mathematical and statistical algorithms, companies can analyze large datasets and identify patterns and trends that can be used to predict future outcomes. And this can be applied to a wide range of business functions as well, from marketing to finance to supply chain management. In this video, we will dive deeper into the world of Predictive Analytics. We will explain the different types of models used in Predictive Analytics and how can they be applied to various business functions. We will also discuss the potential challenges involved in implementing Predictive Analytics and provide tips on how to overcome them as well. So whether you are a business professional or a data analyst, don't miss out on this opportunity to learn more about Predictive Analytics. So let's get started. Having said that, if you want to upskill yourself and want to learn more about Predictive Analytics, then Simply Learn's Postgraduate Program in Data Analytics can be the right option for you. The Postgraduate Data Analytics Program, in partnership with Purdue University, leverages Purdue's academic excellence in data analytics and Simply Learn's collaboration with IBM provides a comprehensive overview of data analytics, its tools and techniques overall. So fast track your career with this comprehensive data analytics program curriculum, which covers the concepts of statistic foundation, analyzing data using Python and R languages, interacting with databases using SQL and visualizing the data using Tableau and Power BI to much more. So why wait? If you want to step further towards a rewarding career in data analytics with Simply Learn's Postgraduate Program in Data Analytics, the course link is added in the description box below. So make sure you check that out. So without any further ado, let's get started with today's topic today. So have you ever experienced the frustration of meticulously planning a long journey only to have your plans disrupted by factors beyond your control? Imagine preparing to leave your home only to find a flat tire on your car or encountering a motorway accident causing a 30 minutes delay. These are just a few examples of the unexpected events that can force you to alter your plans by analyzing historical data, current conditions and various external factors. You can identify these potential risks or disruptions in advance and plan your journey accordingly. So this is where predictive analytics coming to picture to help predict what might happen in the near future. So let us dive deep into what predictive analytics is and how it can be helpful for you. So what is predictive analysis? Now, predict analysis is a branch of data analytics that utilizes historical data, statistical algorithms and machine learning techniques to make predictions and forecast future outcomes. It involves extracting valuable insights from large data sets to identify patterns, trends and relationships that can be used to anticipate future events or behavior. Now, for example, in the context of travel, predictive analytics can be used to analyze historical traffic patterns, weather data and other related factors to estimate the likelihood of congestion, accidents or other delays along a planned route. Now, this information can then be used to adjust travel plans, choose alternative routes or make informed decisions to mitigate the impact of potential disruptions. Now, why predictive analytics is important. Now, predictive analytics has emerged as a vital tool in today's data driven landscape, revolutionizing the way organizations operate and make decisions. So here are the key reasons why predictive analytics is of paramount importance. First one is anticipating the future outcomes. Now, predictive analytics empowers organizations to peer into the future with a higher level of certainty by analyzing historical and real time data, identifying patterns and applying advanced algorithms. Predictive analytics enables accurate predictions and forecasts. This foresight helps organizations make proactive and informed decisions, mitigating any sort of risk, optimizing operation and seizing the opportunities. Strategic decision making. Now, predictive analytics equip decision makers with valuable insights and objective evidence. It enables them to move beyond instincts and subjective judgment, relying instead on data driven predictions. With a comprehensive understanding of the likely outcomes of various choices, organizations can chart a strategy course that maximizes profitability, efficiency, customer satisfaction and overall success. And finally, customer understanding and personalization. In today's hyper competitive market, understanding customers is key to success, right? So predictive analytics help organizations gain a deeper understanding of customer behavior, their preferences and their overall needs. By analyzing vast amounts of customer data, organizations can segment their audience, identify buying patterns and offer personalized experiences, products and other services. This enhances customer satisfaction and ultimately drives revenue growth for the organizations. So moving ahead, let us discuss how does predictive analytics work. So firstly, data scientists or data analysts define the problem. Now, the journey of predictive analytics begin with clearly defining the problem at hand, whether it's fraud detection, inventory optimization or predicting potential flood levels. A well defined problem statement helps determine the appropriate predictive analytics approach to employ. Now, once you define a problem, it is important to acquire and organize data properly. Now we all know data is the lifeblood of predictive analytics. Organizations may have vast amounts of historical data or continually streaming data from various sources like customer interactions. So the first step is to identify and acquire the relevant data and then organize it in a central repository, such as a data warehouse or a BigQuery to facilitate efficient data processing. Now, after that, you need to pre-process data. Now, raw data is rarely is an ideal state for analysis, right? So before developing predictive models, data needs to be pre-processed properly. This involves cleaning the data to remove any kind of anomalies, handling missing data points and addressing extreme outliers that could be caused by errors in input or measurements. Pre-processing ensures that the data is of high quality and ready for model development. Next, you have to develop predictive models. Now, what I mean by that, data scientists or data analysts leverage a range of tools or techniques to develop predictive models based on the problem statement and the nature of the data set. Now, machine learning algorithms, regression models, decision trees, neural networks and much more among the commonly used approaches for this. These models are trained on the prepared data to identify correlations and patterns that can be used for making predictions. And finally, validate and deploy results. Now, model validation is a critical step to assess the accuracy and reliability of predictions. Data scientists rigorously evaluate the model's performance against known outcomes or test data sets. If necessary, adjustments are made to improve the model's accuracy. Once the model achieves satisfactory results, it can be deployed to deliver predictions to stakeholders. This can be done through applications, websites or data dashboards, making the insights easily accessible to decision makers. So by following all these steps, data scientists or data analysts can create robust predictive analytic frameworks that empower organizations to make data-driven decisions, optimize operations and gain a competitive advantage in their respective domains. Now, we have talked about data models a lot previously, right? So what exactly are data models and why we need to use data models in predictive analytics? Now, data models are used in predictive analytics for several reasons. Firstly, understanding data relationships. Now, data models help in understanding the relationship between different variables and entities in a data sets. By defining the structure and connections between data elements, models provide a clear representation of how different factors relate to each other. This understanding is crucial for developing accurate predictive models. Secondly, feature selection. Now, predictive analytics often involves selecting the most relevant features or variables that have a significant impact on the outcome being predicted, right? So data models help in identifying these relevant features by analyzing their relationships, dependencies or any sort of core relationships with the target variable. So this helps in eliminating unnecessary or irrelevant features, reducing noise and improving the accuracy of a predictive model. Thirdly, data preparation and transformation. Now, before applying predictive analytics techniques, data often needs to be pre-processed, transformed and cleaned. Data models provide a blueprint for these data preparation steps. They define the structure and format of data, specify data types, handle missing values and establish rules for data normalization or scaling. Data models ensure that data is in a suitable form for analysis, which is essential for accurate predictions. Next, algorithm selection. Now, different predictive analytics algorithms have specific requirements and assumptions about the structure and format of data. So choosing a right algorithm is a must. So data models provide insights into the characteristics of the data, such as its distribution, scale, relationships, which can guide the selection of appropriate algorithms. For example, linear regression assumes a linear relationship between variables, while decision trees handle categorical variables well. Data models help in aligning these data with the algorithm's requirements as per the need. Next, interpretability and transparency. Data models provide a visual representation of data relationships, making them easier to understand and interpret. This is essentially important for business stakeholders who need to comprehend the underlying factors driving predictions. A well-defined data model allows stakeholders to validate the model's assumptions, evaluate the impact of different variables or outcomes, and gain insights into the decision-making process. And finally, iterative development and improvement. Predictive analytics is often an iterative process where models are built, evaluated, and refiled multiple times. It facilitates this iterative development framework by providing and organizing the documenting the data analysis process. They enable trying changes, documenting assumptions, and storing metadata related to data transformations and feature engineering. So data models help building upon various works and continuously improving the predictive models. So let us now discuss some of the various types of predictive analytical models that you can use. Predictive analytics models are used to analyze historical data and make predictions about future events or outcomes. So there are several types of predictive analytical models, including classification model. Classification models are used to predict categorical outcomes or group data into predefined classes. Examples include logistic regression, decision trees, random forest, support vector machines, and others. Next, we have regression models. Regression models are used to predict a continuous outcome variable based on one or more independent variables. And examples for this include linear regression, multiple regression, and polynomial regression. Third, we have clustering model. Now, clustering models are used to group similar data points together based on their characteristics or patterns. And examples of clustering models include k-means clustering, hierarchical clustering, and much more. Next, we have time series model. Now, time series models are used to predict future values based on patterns and historical time-dependent data. Examples include autoregressive integrated moving average, or in short, ARIMA model and exponential smoothing models. And finally, neural network models. Now, these are some of the advanced kind of predictive analytical models that you can use, such as feed-forward neural networks or recurrent neural networks, RRN or CNN are used to predict complex data patterns and relationships in a data. They're particularly effective for tasks like image recognitions, natural language processing, and sequence predictions. So these are some of the main types of predictive analytical models that you can use. But having all that said, where we can use the predictive analytics in real life. So let us now discuss some of the real-life use cases of predictive analytics, which you can use in your day-to-day basis. So firstly, financial analysis or banking sectors. Now, predictive analytics is used for credit scoring, fraud detection, risk assessment, and investment analysis. It helps financial institutions make data-driven decisions, detect suspicious activities, and identify potential market trends. Now, predictive analytics is also extensively used in sales and marketing as well. Now, predictive analytics plays a crucial role in sales and marketing by providing invaluable insights and help organizations make data-driven decisions. Predictive analytics model analyze historical sales data, market trends, customer behavior, and external factors to forecast future sales, which will in turn help organizations plan their resources, set realistic sales targets, and allocate budget effectively. Now, predictive analytics helps in optimizing production processes, predicting equipment failures, and managing supply chain logistics. By analyzing historical data and real-time information, manufacturers can improve operational efficiency, reduce downtime, and optimize inventory levels. Now, predictive analytics can also be used to identify patients at risk of developing certain diseases, improve diagnosis accuracy, and personalize treatment plans. It also helps hospitals optimize resource allocation, predict patient flow, and forecast disease outbreaks well in advance. And finally, we have retail and e-commerce. Predictive analytics is also used for demand forecasting, inventory management, and customer segmentation. Retailers can predict customer behavior, optimize pricing strategies, and personalize marketing campaigns to enhance customer satisfaction and increase overall sales. So these are some of the real-life applications where predictive analytics can be used. Now, the future of predictive analytics is driven by advancement in technology, the availability of vast amounts of data, and the increasing need for data-driven decision-making. As organizations continue to harness the power of predictive analytics, they can gain a competitive edge, optimize operations, and deliver enhanced customer experience. So that was predictive analytics in a nutshell. I hope you understood that, guys. And with that, we've come to the end of today's session on what is predictive analytics. Thank you for watching the video, guys. If you found this tutorial informative and helpful, give it a thumbs up. And share it with your friends and colleagues. If you have any further queries regarding any of the concepts or topics covered in today's tutorial, feel free to let us know in the comment section below, and our team of experts will be more than happy to help resolve all your queries at the earliest. Until next time, stay safe, thank you, and keep learning.

Speaker 2: Staying ahead in your career requires continuous learning and upskilling. Whether you're a student aiming to learn today's top skills or a working professional looking to advance your career, we've got you covered. Explore our impressive catalog of certification programs in cutting-edge domains, including data science, cloud computing, cybersecurity, AI, machine learning, or digital marketing. Designed in collaboration with leading universities and top corporations and delivered by industry experts. Choose any of our programs and set yourself on the path to career success. Click the link in the description to know more.

Speaker 1: Hi there. If you like this video, subscribe to the Simply Learn YouTube channel and click here to watch similar videos. To nerd up and get certified, click here.