Data Analysis Using ML Models (RandomForestClassifier, DecisionTreeClassifier, LogisticRegression)

📊 Data Analysis Using ML Models — Complete Jupyter Project

This product gives you a ready-to-run, real-world machine learning project for data analysis using Python — built entirely in Jupyter Notebook with clean, well-structured code and a sample dataset in CSV format.

You’ll learn how to take raw data, explore it, preprocess it, train machine learning models, and evaluate results — all step-by-step in a practical workflow.

Whether you're a student, beginner, or data science enthusiast, this project helps you move from theory to hands-on implementation.

🚀 What’s Included

✔ Jupyter Notebook (.ipynb) with fully working code
✔ Clean, commented Python code (easy to follow)
✔ Sample CSV dataset for experimentation
✔ Step-by-step ML pipeline:

• Data loading & inspection
• Data cleaning & preprocessing
• Feature selection
• Model training
• Model evaluation & comparison

✔ Implementation of:

• Logistic Regression
• Decision Tree
• Random Forest

✔ Performance metrics and visualizations

🧠 What You Will Learn

• How to perform exploratory data analysis (EDA) on real datasets
• How to preprocess data for machine learning
• How different ML models behave on the same data
• How to compare models using accuracy and evaluation metrics
• How to interpret results and improve model performance

👨‍💻 Who This Is For

• Python beginners who want a practical ML project
• Students learning Data Science or Machine Learning
• Developers who want a ready example for reference
• Educators looking for teaching material
• Anyone who prefers learning by doing

⚙ Requirements

• Basic Python knowledge
• Jupyter Notebook installed (Anaconda or pip setup)
• Python libraries: pandas, numpy, matplotlib, scikit-learn