This Project is based on Machine Learning which uses Logistic Regression model for predicting whether the object detected by Submarine is Rock or Mine

This model can predict whether an email is spam or not. The logistic regression machine learning algorithm is used to train this model.

This repo desribes bulding a decision tree to predict customer churn in a given organisation

This github repositiory contains the Flight Price Prediction project aims to develop a machine learning model to predict flight ticket prices based on various factors such as departure and arrival locations, dates, airlines, and other relevant features.

OilyGiant mining company finding the best place for 200 new well points, As an Data Scientist we're creating a model who can choose the best 200 point by profit and risk.

Bank Beta Company focus on retain existing customers, our task is to create a model that predicts whether or not a customer will leave the bank soon.

Megaline company wants to develop a model that can analyze consumer behavior and recommend one of Megaline's two new plans: Smart or Ultra. In this classification task, we need to develop a model that is able to choose the right package

This is the Data Mining Project for predicting the student's grade before the final and Mid-2 examination. I use Python and Jupyter Notebook for this Project.

1) Pima_Indians_Diabetes_dataset: 83.11% accuracy. 2) Using ScanPy for gene expressions in scRNA sequencing. 3) Using traditional clustering on RNA dataset 4) Stroke Prediction 95% accuracy on test

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.

Spam Mail Prediction using Machine Learning

* Basis EDA * Handling Null/Missing Values * Handling Outliers * Handling Skewness * Handling Categorical Features * Data Normalization and Scaling * Feature Engineering *Accuracy score *Confusion matrix *Classification report

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.

Content: Machine Learning, KNN concept, Euclidean distance, Data preprocessing, Scaling the data, Performing train-test split, Applying KNeighbors Classifier, Predicting Y_pred based on X_test, Evaluation using Confusion Matrix, Accuracy score, Recall value & Precision, Underfitting & Overfitting, Measures to overcome Underfitting & Overfitting

Content: Machine Learning, Logistic regression steps, Probability matrix, Confusion matrix, Accuracy score, Recall value, Data preprocessing, Label encoding, Scaling the data, Splitting train test data, Running Logistic Regression, Y prediction on test data, Class imbalance, Type 1 & Type 2 errors.

This project develops a deep learning model that trains on 1.6 million tweets for sentiment analysis to classify any new tweet as either being positive or negative.

learning python day 6

Learning python day 4

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

evaluation metrics implementation in Python from scratch