Spam Detector is an online Email/SMS Spam Classifier based binary classification model to detect whether a text message is spam or not (i.e Ham).

Fashion Recommendation Engine using Deep Learning.

It is facial recognition based security system which is based on K-Nearest Neighbours

Fundamental ML algorithms in Python.

Trying to code machine learning model from scratch in python

This repository is for managing all my assignment for Artificial Intelligence Course

A classifier that uses the scikit-learn library to predict whether the given measurements belong to a polar bear or a gray wolf.

K Nearest Neighbor Algorithm implemented in python using numpy

A machine learning project using logical regression and KNN to clasify if a user will interact with an online advertisement

Implemented Artificial Neural Networks, and K nearest neighbors for classification problems. Used cross-validation for improving model accuracy. Plotted different types of learning curves like error rates vs train data size, error rates vs clock time. Compared performance using learning curves and confusion matrices across algorithms.

Lab works on Information Processes Analysis subject. Taught on 1st sem of applied mathematics master programm.

Performance Comparision of Machine Learning Algorithms

This repository will include files of the face recognition project.

Predicting Car prices given the various characteristics

To check the data belongs to which class of Iris plant. (Famous data Set: 'Iris.csv')

In this Machine Learning Top Project Repository, we will solve the industry-based real-time problem, and also we will solve the Kaggle competition.

Spam Detector is an online Email/SMS Spam Classifier based binary classification model to detect whether a text message is spam or not (i.e Ham).

Sentiment Analysis and Model Comparison on Baby Product Reviews

Created a Python program for K Nearest Neighbor Algorithm implementation from scratch. Determined the Euclidean distance between the data points to classify a new data point as per the maximum number of nearest neighbors. Implemented the algorithm on sklearn’s IRIS dataset which achieved an accuracy of 95.56%.

In my machine learning algorithms repository, I've implemented diverse models