malomodaniels/House-Price-Prediction-using-Logistic-Regression

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malomodaniels/House-Price-Prediction-using-Logistic-Regression

malomodaniels

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Analysis-of-2020-2021-EPL-Dataset

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Google-App-Rating

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SQL-Portfolio-Project

hello-world

A repo to keep the ideas flowing...

test_demo

EDA-on-XYZ-Supermarket

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Titanic-Survival-Prediction

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Number_guessing_game

You have 10 guess chance to guess the number between 1 to 100 which is generated randomly. Play with it. If you want to save your score then check the database folder. It will save your score.

911-Calls-Finance-Banks-

These are projects I worked on while starting out Data Analysis with Python Tools

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Excel-Analytics

Portfolio-Website

Utiva-Business-Analysis-Capstone-Project

This repository is for the Utiva Business Capstone Project on Excel Analytics with concepts such as Referencing, Name Ranges, Conditional Formatting and PivotTable

PyCodes

Repository containing the codes I wrote during the early stages of my Python training

Ecommerce-Online-Sales-Prediction-

This is a dunny contract work with an assumed Ecommerce company based in New York City that sells clothing online but they also have in-store style and clothing advice sessions. Customers come in to the store, have sessions/meetings with a personal stylist, then they can go home and order either on a mobile app or website for the clothes they want. The company is trying to decide whether to focus their efforts on their mobile app experience or their website.

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COVID-19-Fake-Tweets-Classification

Fake news is disseminated to intentionally persuade readers to accept biased or untrue beliefs by changing the way people interpret and respond to real news. Detecting fake news manually is relatively tedious especially with the rate at which information is been dispersed on Twitter, hence the need to leverage Machine Learning classifiers for this task. Search for a generally accepted COVID-19 dataset for fake news detection is still on, largely due to its novelty. A novel – more recent, and robust FND Dataset – was curated by scraping tweets from Twitter handles of some health organisations using Twitter API and socialscrapr. The dataset was preprocessed using Python libraries and Microsoft Excel after which it was split into train (80%), validation (10%) and test (10%) datasets and used on SVM, LR, DT baseline Machine Learning algorithms with SVM classifier obtaining the best result with 93.17% for both accuracy and F1 – score performance metrics.

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NLP-Fake-News-Classification

Using the "Fake and Real News Dataset" on Kaggle, the aim of this project is to classify the news article with the aid of Natural Language Processing Techniques. The Decision Tree Classifier is the chosen classifier while both classification_report and accuracy_score were used for evaluating the performance of the model.

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