📊 Machine Learning Tutorial Using Scikit-Learn

Welcome to the Machine Learning Tutorial Using Scikit-Learn repository!
This is the official repository containing all the resources, datasets, Python scripts, and trained models used in my YouTube video series on Machine Learning (ML) with Python and Scikit-Learn (sklearn).

Whether you are a beginner or an intermediate learner, this repository provides everything you need for hands-on practice.

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🔗 YouTube Series

You can follow the full tutorial series on my channel: Ezee Kits
The series covers predictive data analysis, classification, regression, model evaluation, and real-life ML applications using Python.

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📂 Repository Contents

1️⃣ PYTHON FILES

All .py scripts used for the tutorial are included, covering Chapter 1 to Chapter 90.
These scripts demonstrate how to implement various ML concepts such as:

• Data preprocessing
• Model training and evaluation
• Visualization with matplotlib and seaborn
• Working with classification and regression algorithms

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2️⃣ CSV FILES

The repository includes several CSV datasets used throughout the tutorials:

• titanic.csv – Passenger survival data for classification tasks.
• football_predictions_train.csv & football_predictions_test.csv – Data from Prematips for football match outcome predictions.
• stock_trading.csv – Dataset for stock market prediction examples.
• tic_tac_toe.csv – Dataset for game outcome prediction.
• iris.csv – Classic flower dataset for classification.
• email_spam.csv – Email spam detection dataset.
• house_prices.csv – Dataset for regression and price prediction tasks.

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3️⃣ TRAINED MODELS FILES

Saved models for classification and regression tasks:

• .pickle files – Many trained models saved in pickle format.
• .joblib files – Many trained models saved in joblib format for faster loading.

These allow you to load pre-trained models without retraining, ideal for testing or experimentation.

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4️⃣ OTHER FILES

Includes other auxiliary files used in the tutorial videos, such as:

• Text files
• Logs
• Helper scripts
• Any additional resources needed to follow along with the videos

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🧠 What You Will Learn

By exploring this repository, you will learn how to:

• Load and explore datasets using pandas
• Preprocess data for machine learning
• Train and test models using scikit-learn
• Evaluate models with metrics like accuracy, precision, recall, and R²
• Apply ML to real-life examples such as Titanic survival, stock prediction, football match prediction, and more

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⚙️ How to Use This Repository

1. Clone the repository

   git clone https://github.com/Ezee-Kits/Machine-Learning-Tutorial-Using-Scikit-Learn-.git
   cd Machine-Learning-Tutorial-Using-Scikit-Learn-

2. Install Dependencies Make sure Python 3.8+ is installed, then run:

   pip install pandas scikit-learn matplotlib seaborn

3. Open a Python Script Each .py file corresponds to a chapter and contains step-by-step explanations. Run them in your IDE or Jupyter Notebook.

4. Load a CSV Dataset

   import pandas as pd
   df = pd.read_csv('CSV_Files/titanic.csv')
   print(df.head())

5. Load a Trained Model

   import joblib
   model = joblib.load('Trained_Models/logistic_regression_model.joblib')
   predictions = model.predict(df[['Pclass', 'Age', 'SibSp', 'Parch']])
   print(predictions)

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📝 Repository Structure

Machine-Learning-Tutorial-Using-Scikit-Learn-/
│
├── PYTHON_FILES/             # Scripts for Chapters 1-90
│   ├── chapter_01.py
│   ├── chapter_02.py
│   └── ...
│
├── CSV_FILES/                # All datasets used in tutorials
│   ├── titanic.csv
│   ├── football_predictions_train.csv
│   ├── football_predictions_test.csv
│   ├── stock_trading.csv
│   ├── tic_tac_toe.csv
│   ├── iris.csv
│   ├── email_spam.csv
│   └── house_prices.csv
│
├── TRAINED_MODELS/           # Saved ML models
│   ├── model1.pickle
│   ├── model2.joblib
│   └── ...
│
├── OTHER_FILES/              # Other resources and helper files
│   └── ...
│
└── README.md                 # This file

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👨‍💻 Author

Chikwendu Emmanuel Onyedika (Peter)

• YouTube Channel: Ezee Kits
• Topics Covered: Python, Machine Learning, Data Analysis, Scikit-Learn, Real-Life ML Projects

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⚖️ License

This repository is open-source under the MIT License.
You are free to use, modify, and distribute the code for educational purposes.

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🌟 Quick Start Example

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score

# Load dataset
df = pd.read_csv('CSV_FILES/titanic.csv')

# Feature selection and target
X = df[['Pclass', 'Age', 'SibSp', 'Parch']]
y = df['Survived']

# Handle missing values
X['Age'].fillna(X['Age'].mean(), inplace=True)

# Train/test split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Train model
model = LogisticRegression()
model.fit(X_train, y_train)

# Predict & Evaluate
y_pred = model.predict(X_test)
print("Accuracy:", accuracy_score(y_test, y_pred))

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🔗 Useful Links

• YouTube Tutorial Playlist
• Scikit-Learn Documentation
• Pandas Documentation