Here are some fun projects to learn ML using Handson approach
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Updated
Oct 4, 2023 - Jupyter Notebook
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.
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.
Women's clothing Reviews dataset. Exploratory data analysis of various attributes of dataset is performed.https://www.kaggle.com/nicapotato/womens-ecommerce-clothing-reviews
It calculates the accuracy score and confusion matrix for a logistic regression model. The dataset is about coupon used or not in an apparel store known as Simmons .
This performs of evaluation of machine learning models with ease and provides the visualization
evaluation metrics implementation in Python from scratch
A Preprocessing, Analytical and Modeling Case Study using Supervised ML Models
In this project we have created a spam classifier model on UCI dataset. We performed data cleaning and preprocessing, followed by Stemming and Bag of Words technique and finally developed a Naive Bayes Multinomial model.
natural language processing techniques applied on hotel review dataset
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