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.

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).

Predicting if Customers will Default on Loans Using ML Classification Algorithms

This repository contains Machine Learning Classification algorithms implementation

Kaggle competition-Titanic dataset(Prediction of Number of survivors in Titanic disaster)

Compared the metrics and performance of different classification algorithms on Heart Failure dataset from UCI ML Repository

This dataset was used to learn more about how some machine learning models work: KNN, Naive Bayes, and Decision Tree. It also includes some model evaluation metrics: Precision, Recall, Accuracy, and F1-Score. These metrics were derived from the confusion matrix.

This Github repository contains projects related to prediction with Decision Tree. Exploring Insights/Inferences by performing EDA on the given project data (Iphone purchase).

Machine Learning: Group Project

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

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

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

Data analysis on Automobile Dataset

Machine Learning course of Piero Savastano 4: CountVectorizer, BernoulliNB, accuracy_score, pandas