transaction-fraud-detection

A data science project to predict whether a transaction is a fraud or not.

Creator: juniorcl
Stars ⭐️: 118
Forked By: 65
https://github.com/juniorcl/transaction-fraud-detection

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5 Best beginner-friendly data science projects!

1-Loan Approval Prediction
2-Credit Card Fraud Detection
3-Netflix Movies and TV Shows Analysis
4-Sentiment Analysis of Tweets 5-Weather Data Analysis

These projects are ideal for beginners who want to grasp the fundamentals and get closer to solving real-life projects.

How to choose the right portfolio project?

Here are my best tips:

Pick What You Like: Choose a topic you enjoy to keep the project fun.

Show Your Skills: Make sure your project shows off what you can do, like organizing data or making charts.

Keep It Simple: Start with a simple project that you can expand later.

Use Available Data: Choose a project with easy-to-find data.

Data Science Full Course For Beginners 2024

Fundamentals of Data Science: Understand the basics, including data types, data collection, and data cleaning.
Statistics & Probability: Dive into the math that powers data analysis.
Data Visualization: Learn to create insightful visual representations of data.
Machine Learning: Get hands-on with algorithms and models that make predictions based on data.
Tools & Technologies: Master the use of Python, R, SQL, and key data science libraries and frameworks.
Real-World Projects: Apply your knowledge on real data science problems and solutions.

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Python for Data Visualization: The Complete Masterclass

Transforming Data into Insights: A Comprehensive Guide to Python-based Data Visualization

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What is PCA

PCA is a commonly used tool in statistics for making complex data more manageable. Here are some essential points to get started with PCA in R:

🔹 What is PCA? PCA transforms a large set of variables into a smaller one that still contains most of the information in the original set. This process is crucial for analyzing data more efficiently.

🔸 Why R? R is a statistical powerhouse, favored for its versatility in data analysis and visualization capabilities. Its comprehensive packages and functions make PCA straightforward and effective.

🔹 Getting Started: Utilize R's prcomp() function to perform PCA. This function is robust, offering a standardized method to carry out PCA with ease, providing you with principal components, variance captured, and more.

🔸 Visualizing PCA Results: With R, you can leverage powerful visualization libraries like ggplot2 and factoextra. Visualize your PCA results through scree plots to decide how many principal components to retain, or use biplots to understand the relationship between variables and components.

🔹 Interpreting Results: The output of PCA in R includes the variance explained by each principal component, helping you understand the significance of each component in your analysis. This is crucial for making informed decisions based on your data.

🔸 Applications: Whether it's in market research, genomics, or any field dealing with large data sets, PCA in R can help you identify patterns, reduce noise, and focus on the variables that truly matter.

🔹 Key Packages: Beyond base R, packages like factoextra offer additional functions for enhanced PCA analysis and visualization, making your data analysis journey smoother and more insightful.

Embark on your PCA journey in R and transform vast, complicated data sets into simplified, insightful information. Ready to go from data to insights? Our comprehensive course on PCA in R programming covers everything from the basics to advanced applications.

For all Data Engineers out there, here is The State of Data Engineering 2024

Some of the highlights:

✅ More and more, data observability tools are used not just to monitor data sources, but also the infrastructure, pipelines, and systems after data is collected.

✅ Companies are now seeing data observability as essential for their AI projects. Gartner has called it a must-have for AI-ready data.

✅ Like in 2023, Monte Carlo is leading in this area, with G2 naming them the #1 Data Observability Platform. Big organizations like Cisco, American Airlines, and NASDAQ use Monte Carlo to make their AI systems more reliable.