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Hi There


👨‍💻 About Me :

I am a Data Scientist from South Africa.

  • 🔭 I’m a data Scientist.

  • 🔭 I have experience working as python developer ,Data Science Team Lead and software support at accounting company,data scientist at ecommerce company,data manager non profit.

  • 🌱 Exploring Technical Content Writing.

  • ⚡ In my free time, I solve problems on Zindi,writing articles and read tech articles.

  • 📫How to reach me: Linkedin Badge


🛠️ Languages and Tools :

Flask  BitBucket  Pandas  Jupyter  Python  CSS  HTML  SQL Server  GitHub  Postgresql  MySQL  Vscode  AWS 

🔥 My Stats :

GitHub Streak

Top Langs


✍️ Blog Posts :

Lereko Qholosha's Projects

30-days-of-python icon 30-days-of-python

30 days of Python programming challenge is a step by step guide to learn Python programming language in 30 days.

agriculture icon agriculture

A simple ML and DL based website which recommends the best crop to grow, fertilizers to use and the diseases caught by your crops.

analyse-predict icon analyse-predict

A library created to house a package of python functions to analyse and calculate data from Eskom, through processing numeric and text data as Eskom requires certain metrics to be calculated for their analytical team.

bootstrap-5-landing-page-tutorial icon bootstrap-5-landing-page-tutorial

This repository contains a source code to Bootstrap 5 Landing Page tutorial video. Access more Web Development tutorials via the link below

credit-card-challenge icon credit-card-challenge

Logistic Regression on Credit Card Approval Dataset Completed by Lereko Qholosha. Overview: Build a model that will predict the Approval Status given features: Gender, Age, Debt, Married, BankCustomer, EducationLevel, Ethnicity, YearsEmployed, PriorDefault, Employed, CreditScore, DriversLicense, Citizen, ZipCode, Income and finally the ApprovalStatus. Problem Statement: Evaluate the performance of the Logistic Regression Model. Data: Credit Card Approval dataset Deliverables: Write a function which calculates the Accuracy, Precision, Recall and F1 scores. Topics Covered

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