Tevin Temu's Projects
An application on adopting pets based on the intro to intermediate React by Brian Holt .
Africa Data School Jan -March 2022 Cohort Assignments.
The objective for this challenge was to develop a multi-class classification model to classify news content according to six specific categories.
A collection of awesome papers, articles and various resources on credit and credit risk modeling
list of papers, code, and other resources
This curated list contains python packages for time series analysis
ADS Project based on bank policy subscription .
The objective of this challenge is to develop a machine learning model that classifies statements and questions expressed by university students in Kenya when speaking about the mental health challenges they struggle with. The four categories are depression, suicide, alchoholism, and drug abuse.
Notebooks about Bayesian methods for machine learning
The data scientists at BigMart have collected 2013 sales data for 1559 products across 10 stores in different cities. Also, certain attributes of each product and store have been defined. The aim is to build a predictive model and predict the sales of each product at a particular outlet. Using this model, BigMart will try to understand the properties of products and outlets which play a key role in increasing sales.
A retail company “ABC Private Limited” wants to understand the customer purchase behaviour (specifically, purchase amount) against various products of different categories. They have shared purchase summary of various customers for selected high volume products from last month. The data set also contains customer demographics (age, gender, marital status, city_type, stay_in_current_city), product details (product_id and product category) and Total purchase_amount from last month. Now, they want to build a model to predict the purchase amount of customer against various products which will help them to create personalized offer for customers against different products.
Building Large Language Model Applications, Published by Packt
A curated list of practical business machine learning (BML) and business data science (BDS) applications for Accounting, Customer, Employee, Legal, Management and Operations (by @firmai)
This repository contains a machine learning project that aims to detect cancer using the k-Nearest Neighbors (k-NN) algorithm.
A complete computer science study plan to become a software engineer.
coffee prediction streamlit app
Machine Learning Model to determine which user is likely to churn from a subscription after a period of time
EDA and Machine Learning Models in R and Python (Regression, Classification, Clustering, SVM, Decision Tree, Random Forest, Time-Series Analysis, Recommender System, XGBoost)
Repository containing data science projects.
A Case Study Approach to Successful Data Science Projects Using Python, Pandas, and Scikit-Learn
GitHub repo for Dave Langer's YouTube tutorials on data analysis with Excel
This Repository contains the list of various Machine and Deep Learning related projects. Related code and data files are available inside this folder. One can go through these projects to implement them in real life for specific use cases.
A simple Django app to register logs in admin backoffice.
Deep learning for audio processing
build a predictive model to determine if a building will have an insurance claim during a certain period or not. You will have to predict the probability of having at least one claim over the insured period of the building. The model will be based on the building characteristics. The target variable, Claim, is a: 1 if the building has at least a claim over the insured period. 0 if the building doesn’t have a claim over the insured period.
Expresso is an African telecommunications company that provides customers with airtime and mobile data bundles. The objective of this challenge is to develop a machine learning model to predict the likelihood of each Expresso customer “churning,” i.e. becoming inactive and not making any transactions for 90 days. This solution will help Expresso to better serve their customers by understanding which customers are at risk of leaving.