Machine learning project that identifies fraudulent credit card transactions to prevent financial loss.

Twitter Sentiment Analysis

Natural Language Processing (NLP) project that analyzes the sentiment (Positive, Negative, Neutral) of tweets.

Key Features

• Tweet Scraping: Fetches tweets using API or scraper.
• Text Preprocessing: Tokenization, stop-word removal, and stemming.
• Sentiment Classification: Uses Naive Bayes or Transformer models (BERT).
• Visualization: Word clouds and sentiment distribution charts.

Technology Stack

• NLP: NLTK, Spacy, Transformers
• Language: Python
• Model: BERT / RoBERTa

Installation

1. Install Python dependencies.
2. Run the sentiment analyzer on your dataset or live input.

Traffic Sign Recognition System

This project is a state-of-the-art computer vision application designed to automatically recognize and classify traffic signs from images or video streams. Built using deep learning techniques (Convolutional Neural Networks), it achieves high accuracy on the GTSRB (German Traffic Sign Recognition Benchmark) dataset.

Key Features

• Real-time Detection: Capable of processing video feeds for real-time sign recognition.
• High Accuracy: Achieves over 98% accuracy on test datasets using optimized CNN architecture.
• Robust Preprocessing: Includes image enhancement, normalization, and augmentation pipelines to handle varying lighting conditions.
• User Interface: Simple CLI/GUI for testing individual images.

Technology Stack

• Language: Python 3.9+
• Deep Learning: PyTorch / TensorFlow (Configurable)
• Computer Vision: OpenCV
• Data Handling: NumPy, Pandas

Installation

1. Clone the repository.
2. Install dependencies:

   pip install -r requirements.txt
   

3. Run the training script or use the pre-trained model:

   python main.py --predict sample_image.jpg
   

Dataset

This model is trained on the GTSRB dataset, which contains 43 classes of traffic signs.

Movie Recommendation System

Recommends movies to users based on their viewing history and preferences using collaborative filtering.

Key Features

• Collaborative Filtering: User-based and Item-based recommendation.
• Content-Based Filtering: Recommends based on genre and tags.
• Search: Find movies by title.
• Rating Interface: Users can rate movies to improve suggestions.

Technology Stack

• Language: Python
• Algorithm: SVD (Matrix Factorization) / Cosine Similarity
• Dataset: MovieLens

Installation

1. Download the dataset.
2. Run the recommendation engine script.

Customer Churn Analysis

Data analysis project identifying customers likely to cancel their subscription. Helps businesses retain users.

Key Features

• Exploratory Data Analysis (EDA): Visualizes churn factors.
• Predictive Modeling: Classifies users as Churn/No-Churn.
• Feature Importance: Identifies key drivers of churn (e.g., contract type, monthly charges).
• Dashboard: PowerBI/Tableau dashboard file included.

Technology Stack

• Language: Python / R
• Tools: Pandas, Seaborn, Scikit-learn

Installation

1. Run the notebook to generate the model.
2. View the generated insight reports.

Stock Price Prediction (LSTM)

Predicts future stock prices using Long Short-Term Memory (LSTM) recurrent neural networks.

Key Features

• Time-Series Analysis: Handles sequential financial data.
• Data Visualization: Candlestick charts and trend lines.
• Forecasting: Predicts closing price for the next N days.
• Backtesting: Validates model performance on historical data.

Technology Stack

• Deep Learning: Keras / TensorFlow
• Data Source: Yahoo Finance API (yfinance)
• Visualization: Plotly / Matplotlib

Installation

1. Install dependencies.
2. Run train_model.py to train.
3. Run predict.py to forecast.

Credit Card Fraud Detection

Machine learning project that identifies fraudulent credit card transactions to prevent financial loss.

Key Features

• Anomaly Detection: Identifies outliers in transaction patterns.
• Imbalanced Data Handling: Uses SMOTE/ADASYN techniques.
• Model Comparison: Evaluates Random Forest, Logistic Regression, and SVM.
• Metrics: Focuses on Precision-Recall AUC rather than just accuracy.

Technology Stack

• Language: Python
• Libraries: Scikit-learn, Pandas, Matplotlib
• Notebook: Jupyter

Installation

1. Install requirements: pip install pandas scikit-learn jupyter.
2. Launch Jupyter Notebook.
3. Run the analysis.ipynb file.