Machine Learning Projects Collection

A collection of machine learning projects demonstrating various algorithms and their applications.

Table of Contents

1. Financial Forecast (Linear Regression)
2. Credit Card Fraud Detection (Logistic Regression)
3. Movie Recommendation System (KNN)
4. Breast Cancer Detection (Decision Trees)
5. Spam Filter (Naive Bayes)
6. Breast Cancer Detection (SVM)
7. Credit Card Fraud (Random Forest)
8. Image Recognition (Gradient Boosting)
9. House Price Prediction (Bagging)

Project Details

Financial Forecast

Algorithm: Linear Regression

• Purpose: Predict stock prices
• Features: Historical prices, volume, technical indicators
• Output: Predicted future prices
• Key Functions:
  • quandl.get_table(): Fetches financial data
  • LinearRegression(): Creates regression model
  • preprocessing.scale(): Normalizes features

Credit Card Fraud

Algorithm: Logistic Regression

• Purpose: Identify fraudulent transactions
• Features: Transaction amount, time, location
• Output: Fraud probability
• Key Functions:
  • LogisticRegression(): Creates classifier
  • StandardScaler(): Scales features
  • confusion_matrix(): Evaluates model

Movie Recommendation

Algorithm: K-Nearest Neighbors (KNN)

• Purpose: Suggest similar movies
• Features: Movie characteristics, user preferences
• Output: Movie recommendations
• Key Functions:
  • NearestNeighbors(): Creates KNN model
  • kneighbors(): Finds nearest neighbors
  • CountVectorizer(): Processes text data

Breast Cancer Detection

Algorithm: Decision Trees

• Purpose: Classify cancer cases
• Features: Medical measurements
• Output: Malignant/Benign classification
• Key Functions:
  • DecisionTreeClassifier(): Creates tree model
  • tree.plot_tree(): Visualizes decision tree
  • feature_importances_: Shows feature importance

Spam Filter

Algorithm: Naive Bayes

• Purpose: Classify emails as spam/legitimate
• Features: Email content, headers
• Output: Spam probability
• Key Functions:
  • MultinomialNB(): Creates Naive Bayes model
  • CountVectorizer(): Processes text
  • predict_proba(): Provides probabilities

Breast Cancer Detection (SVM)

Algorithm: Support Vector Machine

• Purpose: Classify cancer cases
• Features: Medical measurements
• Output: Malignant/Benign classification
• Key Functions:
  • SVC(): Creates SVM classifier
  • StandardScaler(): Scales features
  • classification_report(): Evaluates model

Credit Card Fraud (Random Forest)

Algorithm: Random Forest

• Purpose: Identify fraudulent transactions
• Features: Transaction details
• Output: Fraud probability
• Key Functions:
  • RandomForestClassifier(): Creates forest model
  • feature_importances_: Shows feature importance
  • predict_proba(): Provides probabilities

Image Recognition

Algorithm: Gradient Boosting

• Purpose: Classify handwritten digits
• Features: Pixel values
• Output: Digit classification
• Key Functions:
  • GradientBoostingClassifier(): Creates model
  • StandardScaler(): Scales features
  • plot_predictions(): Visualizes results

House Price Prediction

Algorithm: Bagging

• Purpose: Predict house prices
• Features: House characteristics
• Output: Predicted price
• Key Functions:
  • BaggingRegressor(): Creates bagging model
  • mean_squared_error(): Evaluates model
  • r2_score(): Calculates R-squared

Common Workflow

1. Data Preparation

   • Load/Create data
   • Handle missing values
   • Feature engineering

2. Model Training

   • Split data (train/test)
   • Scale features
   • Train model

3. Evaluation

   • Calculate metrics
   • Visualize results
   • Make predictions

Project Selection Guide

For Prediction Tasks

• Linear Regression: Simple numerical predictions
• Bagging: Complex numerical predictions

For Classification Tasks

• Logistic Regression: Binary classification
• Decision Trees: Multi-class classification
• Random Forest: Complex classification

For Recommendation Tasks

• KNN: Similar item recommendations

For Text Classification

• Naive Bayes: Spam detection, text categorization

Requirements

pip install pandas numpy scikit-learn matplotlib seaborn

Usage

1. Choose the appropriate project for your task
2. Follow the common workflow
3. Use the provided functions and models
4. Evaluate and visualize results

Contributing

Feel free to contribute to this project by:

1. Adding new machine learning projects
2. Improving existing code
3. Adding more documentation
4. Fixing bugs

License

This project is licensed under the MIT License - see the LICENSE file for details.