aidenerdogan / real_wolrd_ds_and_ml

This repository offers a wide range of data science and machine learning projects, interview home tasks, and comprehensive interview preparations to help both beginners and experienced practitioners master the concepts and enhance their skills.

Real World Data Science and Machine Learning

Welcome to the Data Science and Machine Learning Projects repository! This repository is designed to help you master data science and machine learning concepts through real-world projects, interview home tasks, and comprehensive interview preparations.

Whether you are a beginner looking to kickstart your data science journey or an experienced practitioner aiming to enhance your skills, this repository provides a wealth of resources to support your learning and professional growth.

Features

Real-World Projects:

Explore a diverse collection of data science and machine learning projects that cover a wide range of domains, including finance, healthcare, e-commerce, and more. Each project is carefully curated to showcase practical applications of machine learning techniques and provide hands-on experience in solving real-world problems.

Bike Sharing Trip Generation and Analysis

Customer Segmentation with KNN and RFM

eCommerce Inventory Planning for Multiple Products using Sarimax and Prophet

Fake News Detection

Food Delivery Time Prediction

Real Estate Price Prediction

Interview Home Tasks:

Prepare for data science and machine learning interviews by tackling a variety of home tasks commonly encountered during the interview process. These tasks are designed to assess your problem-solving skills, critical thinking abilities, and understanding of core machine learning concepts. By practicing with these tasks, you can build confidence and enhance your performance in interviews.

Predicting Life Expactancy

Efficient Supply Allocation on Ride Hailing Platform

Interview Preparations:

Access a comprehensive set of resources to help you excel in data science and machine learning interviews. From technical interview questions and coding challenges to algorithm explanations and best practices, this repository covers all facets of interview preparations. Take advantage of the carefully curated content to sharpen your knowledge and become well-prepared for interviews.

Data Scientist and Machine Learning Engineer Interview Preparation

Fuzzy Logic Dishwasher Problem Solving

Bike Sharing Trip Generation and Analysis

This project aimed to address the absence of trip data for the popular Chicago Bike sharing service, Divvy. By capturing regular snapshots of station and bike availability, a custom algorithm was developed to generate trip data. The generated data underwent thorough data wrangling, and the exciting part was analyzing and visualizing the trips on interactive folium maps. This project successfully enabled the exploration of bike movements and user patterns within the Divvy system.

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Customer Segmentation with KNN and RFM

This project aimed to segment customers using K-Nearest Neighbors (KNN) and RFM (Recency, Frequency, Monetary) analysis. By combining these techniques, the project successfully grouped customers based on their similarities and behavior patterns. This segmentation approach allowed businesses to personalize their marketing strategies and improve customer engagement.

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eCommerce Inventory Planning for Multiple Products using Sarimax and Prophet

Build a time series forecasting model that helps eCommerce merchants plan their monthly inventory purchasing for the year 2023 for multiple products on M5-Accuracy Compatation data.

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Fake News Detection

By practicing this real Python NLP project of detecting Fake News, you will easily make a model to make difference between real and fake news.

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Food Delivery Time Prediction

The primary aim of this project is to create a model that predicts food delivery times. Its purpose is to precisely estimate how long it will take for food to reach customers. Accurate delivery predictions can significantly improve customer satisfaction, streamline delivery operations, and boost overall efficiency for food delivery platforms.

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Real Estate Price Prediction

This GitHub project guides users through the process of building a real estate price prediction website. It includes steps such as model creation using scikit-learn and linear regression with the Bangalore home prices dataset from Kaggle. Additionally, a Python Flask server is implemented to handle HTTP requests, and a user-friendly website is developed using HTML, CSS, and JavaScript. The project covers various data science concepts, including data cleaning, feature engineering, and hyperparameter tuning. By following this project, users can gain practical experience in real estate price prediction and web development using popular tools and technologies.

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Predicting Life Expactancy

This is a task to predict life exactancy. For more explanation and the goals understanding;

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Efficient Supply Allocation on Ride Hailing Platform

This task focuses on addressing the challenge of efficient supply allocation on a ride-hailing platform. The goal is to optimize the matching of drivers with rider demand in real-time. By analyzing a dataset of synthetic ride demand data, we aim to propose a solution and build a baseline model. The project also involves designing and deploying the model, communicating recommendations to drivers, and outlining an experiment to validate the solution for live operations. Through this project, we demonstrate expertise in data science fundamentals and product thinking, aiming to enhance the overall performance of the ride-hailing platform.

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Data Scientist and Machine Learning Engineer Interview Preparation

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Fuzzy Logic Dishwasher Problem Solving

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