Review : Data Analysis and Predictions

In today's world of digital transformation, data analysis and predictive modeling are key to business, science, and everyday decision-making. Companies use data analysis to optimize processes, predict trends, and increase profitability. Let's take a look at the most commonly used tools and techniques in this area, their advantages and disadvantages, and how they are applied in real-world situations.

1. Data Analysis and Prediction Tools

1.1 Python (Pandas, Scikit-learn, TensorFlow)

✅ Advantages:

Large community and many libraries

Flexibility when working with different types of data

High scalability for machine learning

❌ Disadvantages:

Higher barrier to entry for beginners

Less visually intuitive than some other tools

Example: An e-commerce company analyzes customer behavior using the Pandas library and predicts future purchase patterns using models from Scikit-learn.

1.2 R (ggplot2, caret, tidyverse)

✅ Pros:

Strong support for statistical analysis

Rich visualization capabilities

Excellent for academic research

❌ Cons:

Weaker integration with production systems

Slightly slower with large datasets

Example: Biological research uses R to analyze DNA samples and predict genetic diseases.

1.3 Power BI / Tableau – Visualization and BI tools

✅ Pros:

Intuitive interface, suitable even for non-programmers

Fast analysis of large datasets

Interactive visualizations

❌ Cons:

Less flexible for advanced analyses

License fees for commercial use

Example: A financial institution uses Power BI to visualize transaction data and predict potential fraud.

1.4 Google BigQuery & SQL – Cloud Data Analysis

✅ Advantages:

Fast processing of large datasets

Easy integration with cloud solutions

SQL as a standard query language

❌ Disadvantages:

Possible costs for large data volumes

Limited for complex ML models

Example: A logistics company analyzes billions of shipping data in real time to optimize delivery routes.

2. Data analysis and prediction methods

2.1 Descriptive analysis (Exploratory Data Analysis - EDA)

Used to understand the structure and properties of data. Includes visualizations, statistical summaries, and missing value detection.

Example: The healthcare sector analyzes patient data to identify the most common diseases in different age groups.

2.2 Predictive modeling (Machine Learning, AI)

Uses algorithms such as linear regression, decision trees, or neural networks to predict future values.

Example: A bank uses a machine learning model to assess the credit risk of customers based on their financial history.

2.3 Classification and Clustering

It is used to divide data into groups. K-means, DBSCAN, and hierarchical clustering are commonly used algorithms.

Example: A marketing firm segments customers into different target groups for personalized advertising.

2.4 Time Series and Trend Prediction

It is used to predict trends based on historical data (ARIMA, Prophet, LSTM).

Example: An energy company predicts electricity consumption based on past data and seasonal fluctuations.

3. Real-World Applications of Data Analytics and Prediction

3.1 Demand Forecasting in Retail

Walmart and Amazon use data analytics to predict product demand and optimize inventory. They use models based on historical sales, weather, and seasonal trends.

3.2 Financial markets and algorithmic trading

Investment firms use predictive models to analyze stock prices, cryptocurrencies, and other financial instruments. For example, hedge funds use neural networks to recognize trading opportunities.

3.3 Medical diagnostics

AI systems in hospitals analyze medical images (CT, MRI) and identify potential diseases with an accuracy of more than 90%. For example, Google Health has developed models that can predict diabetic retinopathy.

3.4 Traffic management and smart cities

Google Maps and Waze use big data analysis to predict traffic jams and optimize routes in real time.

3.5 Fraud detection and cybersecurity

Banks like JPMorgan Chase use AI models to identify suspicious transactions and prevent fraud.

Conclusion: Which tool and method to use?

Purpose                                               Recommended Tool                                       Method

Business Intelligence                           Power BI, Tableau                                          Descriptive Analytics

Machine Learning                                Python (Scikit-learn, TensorFlow)                 Predictive Modeling

Statistical Analysis                               R (tidyverse, caret)                                        Classification, Clustering

Big Data                                               Google BigQuery, SQL                                   Time Series Analysis

Financial Modeling                               Python, Excel, MATLAB                                Algorithmic Trading

Choosing the right tool depends on your specific needs. If you need visualizations for management, Power BI or Tableau are ideal. If you want to build after