Part I (646)
Historical data comes in use to predict future events. Historical data is used to build mathematical models that help in capturing important remarks. After certain corrections and edit, the predictive model is then used as data to predict future occurrence and to be alert about taking actions related to those issues.
1. Web archives, databases, and spreadsheets are used to import data.
- Load data in a CSV file, are included in the data sources.
- Also, national weather data, that shows temperature.
2. Find a way to get your data cleaned, by removing outliners and combining data sources.
- With this format, introduction to creating a single table with energy load, temperature and dew point.
- Accumulate with the help of different data, at the same time.
3. with the help of Statistics, Curve fitting tools or machine learning you will need to develop an accurate predictive model.
- You will need to choose on how and when to use neural network, in building and training a definite model.
- Through your training data, have yourself set with different approaches.
- Forecasting of energy being a complex process with many variables.
4. Combine the model in a loaded system.
- You can move into your production system, once you are well equipped and accurate with the forecasts. Having availability in analytics to software programs or devices, including web apps, servers, or mobile devices.
Predictive analytics techniques:
Compete better – Smarter way of competing for companies is when they take help of predictive analysis. How does that happen? That’s because, they can purchase their existing data, helping them figure out their customers.
You will be able to then focus on highlighting your strengths.
Work out how to better meet demand
- With proper understanding of the usage of the model, you will be able to analyze and predict accuracy of demands for the products.
Exceed expectations
- Customer demand is a must, while forecasting. What really makes your customer come back is the way you get back to their expectations. Offering them with good products or services.
Increase efficiency
- How can you predict if you have enough supplies or if there is any production issue?
- The answers lie, with proper analyzing your existing data.
- You will be able to take necessity steps to limit any negative repercussions.
Better able to reach clients
- By first tracking customer touch point data – when did they contact you and how – you can then use this data to forecast when your customers will be well recognized with social media, willing to read an email you send, and even when they might be more willing to talk with you on the phone.
Predictive Analytics can be used, in different ways.
From mining of data and predictive marketing, to utilizing artificial intelligence and machine learning. Learning about new and variability with statistical ways.
• Streamlining Marketing Campaigns:
Companies can know about their customer purchase, with the help of predictive analytics. Optimization of marketing campaigns, to increase their sources.
• Detecting Fraud: By combining different analytical tools and techniques for predictive analytics, companies can drastically improve pattern detection, allowing them to prevent or catch criminal behavior. Cyber fraud is a growing concern in this digital age – that’s why behavioral analytics should be used for scrutinizing all actions taking place in real-time on a network to detect unusual activities and abnormalities that may lead to zero-day vulnerabilities, fraud, and advanced threats, like ransom ware.
• Improving Operations: Another important use of predictive analysis is to effectively manage resources and inventory by forecasting demand. Hotels use predictive analytics to determine the number of guests in different seasons to optimize occupancy and increase profit. Similarly, airlines use predictive analytics to study consumer trends and set ticket prices accordingly. By using predictive analytics in the right way, organizations can make their operations significantly more efficient.
https://www.edupristine.com/blog/importance-of-predictive-analytics
Another important use of predictive analysis is to effectively manage resources and inventory by forecasting demand. Hotels use predictive analytics to determine the number of guests in different seasons to optimize occupancy and increase profit. Similarly, airlines use predictive analytics to study consumer trends and set ticket prices accordingly. By using predictive analytics in the right way, organizations can make their operations significantly more efficient.