📋 My All Projects

Welcome to my academic portfolio of machine learning projects! This repository highlights the various projects I’ve worked on, along with their key details.

📜 Project List

🚀 Spam Email Classification

Domain: NLP

Libraries Used: Scikit-learn, NLTK, Numpy, Pandas

Short Description: Spam email classification involves automatically categorizing emails as either spam or not spam (ham) using machine learning algorithms.

Implementation Code: 🤖 View Code

🚀 Spam Email Classification Using AvgWord2Vec

Domain: NLP

Libraries Used: Scikit-learn, NLTK, Numpy, Pandas, Seaborn, Matplotlib

Short Description: Spam email classification involves automatically categorizing emails as either spam or not spam (ham) using machine learning algorithms.

Implementation Code: 🤖 View Code

🚀 Content Based Movie Recommendation System

Domain: NLP

Libraries Used: Scikit-learn, NLTK, Numpy, Pandas, Seaborn, Matplotlib

Short Description: A content-based movie recommendation system suggests movies to users based on the characteristics of items they have liked or interacted with, analyzing content features such as genres, keywords, or actors to make personalized recommendations.

Implementation Code: 🤖 View Code

🚀 Books Recommendation System

Domain: NLP

Libraries Used: Scikit-learn, NLTK, Numpy, Pandas, Seaborn, Matplotlib

Short Description: A book recommendation system suggests books to users based on their preferences, past interactions, or similar user profiles, utilizing algorithms to enhance personalized reading suggestions.

Implementation Code: 🤖 View Code

🚀 Male and Female Name Classification

Domain: NLP

Libraries Used: Scikit-learn, TensorFlow, Numpy, Pandas, Seaborn, Matplotlib

Short Description: In this project I implemented a model that can prediction whether a person name is male or female.

Implementation Code: 🤖 View Code

🚀 Cat Vs Dog Classification

Domain: Computer Vision

Libraries Used: Scikit-learn, TensorFlow, Numpy, Pandas, Seaborn, Matplotlib

Short Description: Cat vs Dog classification is a machine learning task where algorithms are trained to distinguish between images of cats and dogs.

Implementation Code: 🤖 View Code

🚀 Cat Vs Dog Classification Using Transfer Learning

Domain: Computer Vision

Libraries Used: Scikit-learn, TensorFlow, Numpy, Pandas, Seaborn, Matplotlib

Short Description: Cat vs Dog classification is a machine learning task where algorithms are trained to distinguish between images of cats and dogs.

Implementation Code: 🤖 View Code

🚀 Face Mask Detection

Domain: Computer Vision

Libraries Used: Scikit-learn, TensorFlow, Numpy, Pandas, Seaborn, Matplotlib

Short Description: Face mask detection is the use of computer vision algorithms to identify and classify whether individuals in images or videos are wearing face masks.

Implementation Code: 🤖 View Code

🚀 Fashion Recommendation System Using Transfer Learning

Domain: Image Processing

Libraries Used: Scikit-learn, TensorFlow, Numpy, Pandas, Seaborn, Matplotlib

Short Description: A fashion recommendation system suggests clothing and accessory items to users based on their preferences, past choices, and style, often utilizing deep learning algorithms.

Implementation Code: 🤖 View Code

🚀 Person Identification and Attendance System

Domain: Computer Vision

Libraries Used: Scikit-learn, TensorFlow, Numpy, Pandas, Seaborn, Matplotlib

Short Description: A Person Identification and Attendance System employs technology, such as facial recognition or biometrics, to identify individuals and track their attendance in various settings.

Implementation Code: 🤖 View Code

🚀 Cotton Plant Disease Prediction

Domain: Computer Vision

Libraries Used: Scikit-learn, TensorFlow, Numpy, Pandas, Seaborn, Matplotlib

Short Description: Cotton plant disease prediction involves using machine learning models to forecast or identify diseases affecting cotton plants based on input features like images or sensor data.

Implementation Code: 🤖 View Code

🚀 Which Bollywood Celebrity Are You Using Transfer Learning

Domain: Computer Vision

Libraries Used: Scikit-learn, TensorFlow, Numpy, Pandas, Seaborn, Matplotlib

Short Description: In this project I implemented a model that can recognize in which Bollywood Celebrity you are look like.

Implementation Code: 🤖 View Code

🚀 CIFAR-10 Object Recognition using ResNet50

Domain: Computer Vision

Libraries Used: Scikit-learn, TensorFlow, Numpy, Pandas, Seaborn, Matplotlib

Short Description: CIFAR-10 Object Recognition is a machine learning dataset and task involving the classification of images into 10 different classes, commonly used for training and evaluating image recognition algorithms.

Implementation Code: 🤖 View Code

🚀 Digits Classification

Domain: Image Processing

Libraries Used: Scikit-learn, TensorFlow, Numpy, Pandas, Seaborn, Matplotlib

Short Description: Digits classification is a machine learning task where algorithms are trained to identify and categorize handwritten or digital digits (0-9) into their respective classes.

Implementation Code: 🤖 View Code

🚀 Customers Churn Prediction

Domain: CRM

Libraries Used: Scikit-learn, TensorFlow, Numpy, Pandas, Seaborn, Matplotlib

Short Description: Customer churn prediction involves using data and analytics to forecast which customers are likely to discontinue using a product or service, enabling businesses to proactively retain customers.

Implementation Code: 🤖 View Code