Python-Based Automated Data Cleaning & Validation Tool

Project Overview:

A Python-based script that automates the cleaning and validation of datasets. This tool helps preprocess raw datasets by handling missing values, removing duplicates, standardizing data formats, and detecting outliers.

Key Features:

• Loads and processes CSV files

• Handles missing values (fill, drop, or flag inconsistencies)

• Removes duplicate records automatically

• Standardizes text formats and date structures

• Detects outliers using statistical methods (IQR, Z-score)

• Generates a summary report of transformations

Tech Stack:

• Python (Pandas, NumPy)

• Argparse (Command-line execution)

• JSON/CSV (Report generation)

GitHub Repository:

• Link to Repository

Python-Based Sentiment Analysis on Customer Reviews

Project Overview:

• This Python script performs sentiment analysis on customer reviews, classifying them as positive, neutral, or negative using NLP techniques.

Key Features:

• Loads dataset of customer reviews (CSV format)

• Preprocesses text (removes stopwords, punctuation, lowercase conversion)

• Applies sentiment analysis using VADER (NLTK)

• Categorizes reviews into Positive, Neutral, and Negative

• Saves the processed results to a new CSV fil

Tech Stack:

• Python (NLTK, TextBlob, Pandas)

• Matplotlib (Optional) for visualizing sentiment distribution

GitHub Repository:

• Link to Repository

AI-Powered Fraud Detection System

Project Overview:

This Python-based fraud detection system leverages machine learning to identify potentially fraudulent transactions. It extracts data from an SQL database, performs preprocessing, and applies predictive analytics using Random Forest and Gradient Boosting classifiers. The system is integrated with Streamlit for real-time fraud predictions and automated alerts.

Key Features:

• Loads transactional data from an SQLite database

• Performs feature engineering (e.g., transaction hour, high-amount flag, customer spending behavior)

• Trains multiple classification models (RandomForest & GradientBoosting) for fraud detection

• Evaluates models with accuracy scores and confusion matrices

• Deploys an interactive UI using Streamlit for real-time fraud predictions

• Sends fraud alerts via API when suspicious transactions are detected

Tech Stack:

• Python (Pandas, NumPy, Scikit-Learn, Matplotlib, Seaborn)

• Machine Learning (Random Forest, Gradient Boosting)

• Database (SQLite for storing transaction records)

• Data Visualization (Matplotlib, Seaborn for insights)

• Streamlit (Web-based fraud prediction UI)

• API Integration (Sends fraud alerts via HTTP requests)

GitHub Repository:
Link to Repository

How to Contribute

Interested in improving these projects? Fork the repository, create a new branch, and submit a pull request with your enhancements!

🚀 Stay tuned for more Python-based projects!