What are the best ways to demonstrate your ability to develop predictive models using data mining techniques?

1. Choose a challenging problem that demonstrates your special capabilities. 2. Take any available data that may relate to the problem. 3. Use a strong "pattern cracking" technique (I use my own tool - GT data mining) 4. Find the data inherent patterns. Extract their characteristics and key drivers. Use them for a universal & simple format and upload to the PREDICTION FORMULA. 5. Test the formula and get your client's approval. Use the same testing method for test the data. Use miss-predictions for model update. 6. Be lucky enough to have a noncompetitive client that backs your good results. 7. Pack the extra discoveries and insights that are left out of the prediction project, and propose them as a next stage project.

Definitely! Let's consider finance. A relevant challenge could be predicting credit risk for a lending institution. Utilising data mining, one can analyse historical financial data, customer behavior, and economic indicators to create a robust predictive model. This not only helps in minimising default risks but also optimised lending decisions, showcasing the practical application of data mining in a business-critical scenario. Also, it's important to fully understand the data you have. As deep understanding helps refine your model and shows that you're thorough in solving problems. Combining your knowledge of the field with a careful look at the data makes your predictive modeling more effective. #DataMining #FinanceAnalytics"

Choosing a mission-critical problem for the business is important. The problem should directly impact the organisation's profitability, growth or core objectives. The value of data mining becomes important when it contributes to profit, growth or core objectives. Speed to deliver is also important. If it takes too long to deliver, the business may choose to walk away.

Working with data mining problems involves taking into account the exploration of massive data that improve the KPI or the business roi while discovering patterns. The most interesting industry problems to address might be the following: Direct marketing that is by targeting a group of populations based on their demographic characteristics or another feature that can be age , gender,… Risk management and analysis : bank and insurance. Perhaps it may be looking to grant a loan for a client or not. Stock management: an interesting area is where to have the ability to use data mining techniques to uncover where to order a product and what quantities?

The best way is to start with an already solved challenge on the web. Try to understand the problem, propose your own solution, compare and get your feedback, then repeat with as many challenges as you want. Once you feel confident, you can try to participate in a competency in for example in kaggle.com. That will help you to think in solutions for real problems and at the same time check other people's solutions.

Understanding data is the first step to constructing predictive models using data mining. Consider a healthcare scenario where patient readmission prediction is critical. Explore multiple data sources like electronic health records to ensure data quality and format uniformity. To manage missing numbers and outliers, clean and preprocess data. Use descriptive statistics and visualization to understand data distribution and pick features. Identifying patient readmission and pertinent characteristics, including previous hospitalizations, chronic diseases, and demographics, is crucial in healthcare. A good prediction model starts with this thorough understanding.

Understanding data is fundamental for effective predictive modeling. 1. **Data Quality:** High-quality data is crucial for accurate predictions. Else biased models and unreliable predictions. 2. **Feature Selection:** Choosing relevant features enhances model performance. Understanding the domain is important. 3. **Data Exploration:** Uncovering patterns and relationships in the data Exploratory Data Analysis (EDA) 4. **Handling Missing Data:** Missing data can impact model training. 5. **Data Scaling and Normalization:** Ensures features are on a similar algorithms are sensitive to feature scales, impacting model convergence and performance. 6. **Dealing with Imbalanced Data:** Imbalanced classes can lead to biased models.

I would say without understanding the data we cannot use any type of data mining techniques so should have actual knowledge of data and what it says that us

Performing data exploration in data mining requires a knowledge extraction process that can be defined as the core of the mining process. Furthermore, knowledge discovery in databases is a thorough process conducted by experts in the field to ensure data is : extracted, cleaned , pretreated, mined and prepared for further visualization and presentation. In the first step, it’s usually about the target information to be explored. Second , is to carry on with data or information that can be applied data mining technics. In this pretreatment, it’s essential to know what transformations to make in order to have useful data format ( selection of columns for example) . . . Last step involves error estimate and model or information evaluation.

We have to understand the data, and data types when we are doing data mining because we will get both structured and unstructured data. Have to make sure to convert all data points into reliable manner because machine have to grab our dataset. So for that we have to do some data cleaning and data sanity check. Then have to do some basic analysis like mean median mode and outliers removal etc...then we will get a cleaned data

I will only add that there is not just one way to resolve a ploblem. You may want to try more than one model or even it may be necessary to mix more than one procedure to get to a more accurate solution. The importan thing here is never forget the result is a prediction, it will never be 100% accurate, but you need to try to get the most accurate solutions, but that at the same time is cost effective, for that, you test your models and compare the results.

Depending on your problem, there are different paths to consider to apply those data 📈 mining methods. Thinking about the client paying back the loan or defaulting is saying something about a supervised approach. The classes are labeled beforehand. Main algorithms used in this case are decision trees, naïve bayes, regression,… When the problem approach is not targeting anything as in the first case. Clustering emerges naturally from data. As an example, grouping certain demographic groups to better leverage ads targeting. Thence, clustering algorithms might be used and associations rules learning or even a neural network model.

In today's world, model building has become rather easier compared to the past. E.g. tools like Robyn allow anyone to build a robust marketing mix model. But communicating the results and deriving the correct inferences is the hardest skill since a tool can't help with that. Building the analysis reports and supporting the inferences with dashboard s, charts is critical to take the audience on a journey and help them understand the results.

I think this is the part that your users most interested in. They only care about how your work is going to help to boost the task without your work. They do not quite care about the fancy methods or models you use or the fancy performance. If the performance claims to be good but in fact the model sucks in real life. You users may chase you down and kick your ***.

In today's data world we have to speak with dataset and columns without talking them we can't interpret and analyse the data. We have to do some data analysis like adding new columns by using other columns then we have to create a proper charts and visuals have to check the trend line for example footprints 👣 in the shopping mall, so by using line chart we will create a trend which will shows a count of footprints for that shopping mall day wise. Based on that we will predict interpret the count of audience or customer we got.

Seeking feedback is an important step. Prioritising feedback sources is key. Feedback from analysts has important value as they benefit the predictive models the most. While feedback from management is important, addressing analysts' concerns should take precedence before addressing those from management.