Here are some fun projects to learn ML using Handson approach

This Github repository contains cross selling of health insurance customers on vehicle insurance product. We have to predict whether a customer would be interested in Vehicle Insurance or not by building a ML model. Exploring Insights/Inferences by performing EDA on the given project data. Finding the high accuracy

This Github repository contains projects related to Logistic regression. Exploring Insights/Inferences by performing EDA on the given project data (Bank Term deposit).

Revolutionize customer feedback analysis with our NLP Insights Analyzer. Utilize cutting-edge text preprocessing to transform raw reviews into a machine-friendly format. Explore sentiment models, such as Logistic Regression and Naive Bayes, employing cross-validation for model robustness.

The model should predict whether is it going to rain the next day coming or it isn't. The models that have been deployed were TensorFlow Sequential, Random Forest Classifier and GradientBoostingClassifier. The best model on both training and test set was achieved with Gradient Boosting Classifier with 95.2% and 85.5% accuracy on the train and test.

Exploratory data analysis exercises to understand the main characteristics of a given data set before performing more advanced analysis or further modeling

The purpose of this project is to develop and compare two machine learning models to detect spam emails. Spam detection is a crucial task in email filtering systems to protect users from unwanted and potentially harmful emails. The project involves using a dataset containing various features extracted from email content.

The Email Spam Model project aims to build a machine learning model that can classify emails as spam or not spam (ham). The project uses various text processing techniques and machine learning algorithms to achieve accurate predictions.

Predicting if Customers will Default on Loans Using ML Classification Algorithms

This repository contains Machine Learning Classification algorithms implementation

Machine Learning: Group Project

Extract data provided by lending club, and transform it to be useable by predictive models.

In this project, we aim to identify different fruits: apples, bananas, oranges, and tomatoes; through different Machine Learning algorithms: CNN, XGBoost, InceptionV3 transfer learning, and VGG16 transfer learning

This model predicts the strength of the password by using NLP ( TF-IDF ).The purpose of using tf-idf is to reduce the influence of tokens that are experimentally less informative than characteristics that appear in a small portion of the training corpus and occur often in a particular corpus.

This project aims to build classification models which can be used by financial institutions to detect fraud credit card transactions

Explore the vast field of Natural Language Processing (NLP) with our comprehensive toolkit. From text preprocessing to advanced sentiment analysis and language modeling, this repository provides a range of tools and algorithms to empower your NLP projects. Dive into state-of-the-art techniques and resources curated to enhance your understanding.

Implementation of trading strategy by comparing different machine learning algorithms' accuracies.

In this project, we will be creating a Fake news classifier model that will classify the news based on the 'title' and 'text', whether it is 'Real' or 'Fake'. The dataset that we are using here is taken from www.kaggle.com.

The recommendation system puts together certain users in a group who have read and rated similar books before and recommends books to the users in that group based on the ratings given by the other members of the group. The first section of the code presents the top 50 most popular books on the platform.

machine learning projects based on classification and regression algoithms