Machine Learning Projects: Heart Attack & Conflict Analysis (Udemy.com)

Machine Learning, python, statistics, data science,  machine learning python, python data science, machine learning a-z, data scientist, r, python for data science |

Hello there,

Welcome to the “ Machine Learning Projects: Heart Attack & Conflict Analysis ” course.

Data Science & Machine Learning - Boost your Python & statistics skills with Heart Attack and Global Conflict projects

Machine learning describes systems that make predictions using a model trained on real-world data. For example, let's say we want to build a system that can identify if a cat is in a picture. We first assemble many pictures to train our machine learning model. During this training phase, we feed pictures into the model, along with information about whether they contain a cat. While training, the model learns patterns in the images that are the most closely associated with cats. This model can then use the patterns learned during training to predict whether the new images that it's fed contain a cat. In this particular example, we might use a neural network to learn these patterns, but machine learning can be much simpler than that.

A machine learning course teaches you the techniques and concepts behind the predictive text, virtual assistants, and artificial intelligence. You can develop the foundational skills you need to advance to building neural networks and creating more complex functions through the Python and R programming languages. Machine learning training helps you stay ahead of new trends, technologies, and applications in this field.

We have more data than ever before. But data alone cannot tell us much about the world around us. We need to interpret the information and discover hidden patterns. This is where data science comes in. Data science uses algorithms to understand raw data. The main difference between data science and traditional data analysis is its focus on prediction. Data science seeks to find patterns in data and use those patterns to predict future data. It draws on machine learning to process large amounts of data, discover patterns, and predict trends. Data science includes preparing, analyzing, and processing data. It draws from many scientific fields, and as a science, it progresses by creating new algorithms to analyze data and validate current methods.

Data science application is an in-demand skill in many industries worldwide — including finance, transportation, education, manufacturing, human resources, and banking. Explore data science courses with Python, statistics, machine learning, and more to grow your knowledge. Get data science training if you’re into research, statistics, and analytics.

DATA SCIENCE CAREERS ARE SHAPING THE FUTURE

• Did you know data science needs will create 11.5 million job openings by 2030?

• Did you know the average salary is $100,000+ for data science careers?

Data science experts are needed in almost every field, from healthcare to global geopolitical risk analysis, finance, and government security. Millions of businesses rely on big data to succeed. Therefore, data science careers remain in exceptionally high demand.

WHY IS THIS COURSE UNIQUE? (2 COMPLETE REAL-WORLD PROJECTS)

If you want to learn one of the employer’s most requested skills...

If you are curious about Data Science and looking to start your self-learning journey into the world of data with Python...

If you are an experienced developer looking for a smooth landing in Data Science...

In all cases, you are at the right place!

Unlike purely theoretical courses, we built this course around two massive, real-world projects that take you step-by-step from raw data to model deployment:

1. PROJECT 1: Heart Attack Prediction Analysis

   Focuses on medical data, classification, correlation analysis, and foundational algorithms like Logistic Regression, Decision Trees, SVM, and Random Forest.

2. PROJECT 2: Global Conflict Analysis & Machine Learning (NEW!)

   A massive, multi-level project exploring real-world geopolitical risk data. You will master 4 levels of Exploratory Data Analysis (EDA), advanced Plotly animations, PCA dimensionality reduction, spatial-temporal risk mapping, and state-of-the-art ensemble models like CatBoost and Gradient Boosting.
   

WHAT YOU WILL LEARN ACROSS THE COURSE

In this course, we start from the absolute basics and guide you all the way to advanced model deployment using Python and its powerful libraries like Pandas, NumPy, Seaborn, Plotly, Scikit-Learn, CatBoost, and XGBoost.

Before each section, there is a theoretical concept review, immediately followed by hands-on practical implementation.

PROJECT 1: Heart Attack Prediction Analysis

• Setting up Jupyter Notebooks & understanding the dataset

• Handling missing & unique values

• Uni-variate & Bi-variate EDA: Distplots, Pie Charts, FacetGrid, Swarm Plots, Box Plots, and Heatmaps

• Feature scaling (Robust Scaler) & Outlier handling

• Classification Modeling: Logistic Regression, Decision Trees, Support Vector Machines (SVM), and Random Forest

• Cross-Validation, ROC-AUC metrics, and Hyperparameter Tuning with GridSearchCV

PROJECT 2: Global Conflict Analysis & Machine Learning (NEW 10+ HOUR MODULE)

• Environment Setup: Installing Anaconda Distribution for Windows, macOS, and Linux; mastering Jupyter Lab and Google Colab

• Data Preparation: Multi-stage cleaning, value consistency, and variable renaming

• 4-Level Exploratory Data Analysis (EDA):

  • Level 1 & 2: Distribution trends, regional death analysis, and correlated metrics

  • Level 3 & 4: Clustering via K-Means, Elbow Method & Silhouette Scores, interactive Plotly animated trends, heatmaps, and statistical normality tests (Shapiro-Wilk)

• Advanced Pattern Discovery: PCA (Principal Component Analysis) for dimensionality reduction & outlier-filtered clustering

• Temporal & Spatial Risk Analysis: Spatial risk mapping over time and tracking year-on-year cumulative conflict growth

• Advanced Machine Learning & Deployment:

  • Target engineering & Feature selection

  • CatBoost Modeling & Hyperparameter Optimization